cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p95tdka4
title: National scale assessment for rare earth elements in dissolved water samples and supporting geochemistry

description: This dataset is in support of the publication "The addition of 144Nd to routine ICP-MS analysis as a quick screening tool for approximating earth elements (Q-STAR) in natural waters". It contains inductively coupled plasma mass spectrometry (ICP-MS) data from samples analyzed at the National Water Quality Laboratory (NWQL) from April 2021-May 2022. Data listed include estimated neodymium (Nd) concentrations and site numbers.Site numbers can be used to search published USGS data bases such as the National Water Information System, NWIS for previously published sample information and is shown here to highlight unpublished estimated Nd concentrations from this work. A set of archived samples from NWQL analyses were quantitatively measured for rare earth elements at both the Army Corps of Engineers Engineering Research and Development Center Environmental Laboratory (ERDC-EL) in Vicksburg, MS and the USGS Boulder Common Services Lab in Boulder, CO (BCSL) using ICP-MS. Dissolved organic carbon (DOC) concentrations were available for a subset of archived samples and were analyzed at NWQL. Finally, samples collected from Colorado Springs, CO, Estes Park, CO and Leland, MS were analyzed for rare earth elements using ICP-MS.
DOI: 10.5517/ccdc.csd.cc2h30bz
title: CCDC 2296800: Experimental Crystal Structure Determination

description: Related Article: Santosh J. Gharpure, Sanyog Kumari|2023|Chem.Commun.|60|538|doi:10.1039/D3CC05237K
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.cc2h30d1
title: CCDC 2296802: Experimental Crystal Structure Determination

description: Related Article: Santosh J. Gharpure, Sanyog Kumari|2023|Chem.Commun.|60|538|doi:10.1039/D3CC05237K
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.cc2h30g3
title: CCDC 2296804: Experimental Crystal Structure Determination

description: Related Article: Santosh J. Gharpure, Sanyog Kumari|2023|Chem.Commun.|60|538|doi:10.1039/D3CC05237K
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.cc2h5fhm
title: CCDC 2299130: Experimental Crystal Structure Determination

description: Related Article: Giuseppe Gentile, Beatrice Bartolomei, Jacopo Dosso, Nicola Demitri, Giacomo Filippini, Maurizio Prato|2024|Chem.Commun.|60|602|doi:10.1039/D3CC05176E
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.cc2h7vzl
title: CCDC 2301470: Experimental Crystal Structure Determination

description: Related Article: Teresa Eisner, Arseni Kostenko, Fiona J. Kiefer, Shigeyoshi Inoue|2024|Chem.Commun.|60|558|doi:10.1039/D3CC05090D
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.cc2h7w0n
title: CCDC 2301471: Experimental Crystal Structure Determination

description: Related Article: Teresa Eisner, Arseni Kostenko, Fiona J. Kiefer, Shigeyoshi Inoue|2024|Chem.Commun.|60|558|doi:10.1039/D3CC05090D
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.cc2h7w1p
title: CCDC 2301472: Experimental Crystal Structure Determination

description: Related Article: Teresa Eisner, Arseni Kostenko, Fiona J. Kiefer, Shigeyoshi Inoue|2024|Chem.Commun.|60|558|doi:10.1039/D3CC05090D
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.cc29jdmw
title: CCDC 2130928: Experimental Crystal Structure Determination

description: Related Article: Haolong Wang, Pengyuan Gao, Tengfei Cui, Dongqi Wang, Jinping Liu, Hui He, Zongyuan Chen, Qiang Jin, Zhijun Guo|2023|Dalton Trans.|53|601|doi:10.1039/D3DT03194B
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: 10.5281/zenodo.10493110
title: FLORIS Time Series for Wind Energy Science Paper: The Value of Wake Steering Wind Farm Flow Control in US Energy Markets

description: This dataset contains hourly time series of wind direction, wind speed, and FLORIS-modeled wind plant power for the years 2018-2020 used in the Wind Energy Science paper "The value of wake steering wind farm flow control in US energy markets." The following variables are included in the time series files for each of the 15 wind plants evaluated in the paper:



Time (GMT)

Wind Direction from ERA5 (degrees): The wind direction obtained at the wind plant location from the ERA5 reanalysis dataset

Debiased Wind Speed from ERA5 (m/s): The wind speed at the wind plant location obtained from the ERA5 reanalysis dataset corrected using the debiasing process explained in the paper

Baseline Power for Variable TI (MW): FLORIS-modeled baseline wind plant power with zero yaw misalignment for variable turbulence intensity

Power without Wake Losses for Variable TI (MW): Ideal FLORIS-modeled wind plant power with zero yaw misalignment without wake losses for variable turbulence intensity

Power with Wake Steering for Variable TI (MW): FLORIS-modeled wind plant power with optimal yaw offsets to maximize power production for variable turbulence intensity

Baseline Power for Constant 9% TI (MW): FLORIS-modeled baseline wind plant power with zero yaw misalignment for a constant turbulence intensity of 9%

Power without Wake Losses for Constant 9% TI (MW): Ideal FLORIS-modeled wind plant power with zero yaw misalignment without wake losses for a constant turbulence intensity of 9%

Power with Wake Steering for Constant 9% TI (MW): FLORIS-modeled wind plant power with optimal yaw offsets to maximize power production for a constant turbulence intensity of 9%


The FLORIS results for variable turbulence intensity were obtained using wind speed-dependent turbulence intensities based on the normal turbulence model definition in the IEC 61400-1 wind turbine design standard. However, as discussed in the paper, the turbulence intensities are scaled to achieve the same annual baseline wake losses predicted by FLORIS using a constant turbulence intensty of 9%.
DOI: 10.3886/e197301
title: Evaluating the psychometric properties of the Hispanic Stress Inventory-2 Simplified Version among a diverse sample of Latinos who smoke

description: Objective

Acculturation stress can negatively impact Latinos immigrant mental
and physical health related behaviors such as smoking. It is essential to have
validated and updated instruments that allow the evaluation of acculturation
stress on this population. This study aims to evaluate the psychometric
properties of an abbreviated version of the Hispanic Stress Inventory Version 2
(HSI2) immigration scale among Latinos
who smoke. 

Methods

The study consisted of a secondary data analysis from a baseline
assessment of Decídetexto, a mobile health (mHealth) smoking cessation
randomized clinical trial. Of 457 Latinos included in the parent study, 352
immigrants who smoke were included. Construct validity was analyzed by
completing a Pearson correlation coefficient matrix. Structural validity was
analyzed using an Exploratory Factor Analysis (EFA). Cronbach alpha analysis
was used to estimate the internal consistency of the items constituting a factor.


Results

The results included an abbreviated version of the HSI2 including 52
items. From the Pearson correlation coefficient matrix with a cutoff point of
0.4, 22 of the 52 items were excluded. From the Pearson correlation coefficient
matrix with a cutoff point of 0.4, 22 items were excluded. Exploratory Factor
Analysis (EFA) results in six factors extracted, explaining 69.1% of the variance. According to the EFA,
two items were relocated in different factors from the original scale. The HSI2
30 items scale reflected excellent reliability with a Cronbach’s alpha
coefficient of 0.93. The six factors reflect acceptable to excellent
reliability, ranging from 0.77 - 0.93 across factors. The median for the HSI2
total score was 34.00 (25-45) out of a possible total score of 150. 

Conclusion

Results
confirmed acceptable psychometric properties of the HSI2 simplified 30-item
version and provided a reliable and shorter measure of acculturation stress for
Latinos groups. Having a valid and reduced measure of acculturation stress is
the first step in understanding diverse ethnic groups of Latinos that are at
higher risk of presenting health risk behaviors such as smoking. The present
results provided the possibility of assessing the impact of acculturation
stress among adults who smoke.
DOI: 10.3886/e197301v1
title: Evaluating the psychometric properties of the Hispanic Stress Inventory-2 Simplified Version among a diverse sample of Latinos who smoke

description: Objective

Acculturation stress can negatively impact Latinos immigrant mental
and physical health related behaviors such as smoking. It is essential to have
validated and updated instruments that allow the evaluation of acculturation
stress on this population. This study aims to evaluate the psychometric
properties of an abbreviated version of the Hispanic Stress Inventory Version 2
(HSI2) immigration scale among Latinos
who smoke. 

Methods

The study consisted of a secondary data analysis from a baseline
assessment of Decídetexto, a mobile health (mHealth) smoking cessation
randomized clinical trial. Of 457 Latinos included in the parent study, 352
immigrants who smoke were included. Construct validity was analyzed by
completing a Pearson correlation coefficient matrix. Structural validity was
analyzed using an Exploratory Factor Analysis (EFA). Cronbach alpha analysis
was used to estimate the internal consistency of the items constituting a factor.


Results

The results included an abbreviated version of the HSI2 including 52
items. From the Pearson correlation coefficient matrix with a cutoff point of
0.4, 22 of the 52 items were excluded. From the Pearson correlation coefficient
matrix with a cutoff point of 0.4, 22 items were excluded. Exploratory Factor
Analysis (EFA) results in six factors extracted, explaining 69.1% of the variance. According to the EFA,
two items were relocated in different factors from the original scale. The HSI2
30 items scale reflected excellent reliability with a Cronbach’s alpha
coefficient of 0.93. The six factors reflect acceptable to excellent
reliability, ranging from 0.77 - 0.93 across factors. The median for the HSI2
total score was 34.00 (25-45) out of a possible total score of 150. 

Conclusion

Results
confirmed acceptable psychometric properties of the HSI2 simplified 30-item
version and provided a reliable and shorter measure of acculturation stress for
Latinos groups. Having a valid and reduced measure of acculturation stress is
the first step in understanding diverse ethnic groups of Latinos that are at
higher risk of presenting health risk behaviors such as smoking. The present
results provided the possibility of assessing the impact of acculturation
stress among adults who smoke.
DOI: 10.5281/zenodo.4541462
title: Data for "Magnetoencephalographic Signatures of Conscious Processing Before Birth"

description: Data uploaded here belongs to the manuscript "Magnetoencephalographic Signatures of Conscious Processing Before Birth" (Moser, J., Schleger, F., Weiss, M., Sippel, K., Semeia, L., & Preissl, H. (2021). Magnetoencephalographic signatures of conscious processing before birth. Developmental cognitive neuroscience, 49, 100964). It contains an overview over the study participants and processed MEG data, used for the analysis described in the manuscript. The content of the folder is further described in a README text file. The methods section of the manuscript contains further necessary information. Additional code can be found on Github: https://github.com/moser297/fMEG_data_processing/tree/main/Code_DCN_2021_fetal. For more information or any clarification or additional data/code, please contact the author.
DOI: 10.5281/zenodo.4541463
title: Data for "Magnetoencephalographic Signatures of Conscious Processing Before Birth"

description: Data uploaded here belongs to the manuscript "Magnetoencephalographic Signatures of Conscious Processing Before Birth" (Moser, J., Schleger, F., Weiss, M., Sippel, K., Semeia, L., & Preissl, H. (2021). Magnetoencephalographic signatures of conscious processing before birth. Developmental cognitive neuroscience, 49, 100964). It contains an overview over the study participants and processed MEG data, used for the analysis described in the manuscript. The content of the folder is further described in a README text file. The methods section of the manuscript contains further necessary information. Additional code can be found on Github: https://github.com/moser297/fMEG_data_processing/tree/main/Code_DCN_2021_fetal. For more information or any clarification or additional data/code, please contact the author.
DOI: 10.34740/kaggle/dsv/7385311
title: crypto-data-11-01-24
DOI: 10.5281/zenodo.4018826
title: Data for "Magnetoencephalographic Signatures of Hierarchical Rule Learning in Newborns"

description: Data uploaded here belongs to the manuscript "Magnetoencephalographic Signatures of Hierarchical Rule Learning in Newborns" (Moser, J., Schleger, F., Weiss, M., Sippel, K., Dehaene-Lambertz, G., & Preissl, H. (2020). Magnetoencephalographic signatures of hierarchical rule learning in newborns. Developmental cognitive neuroscience, 46, 100871). It is processed MEG data and an R script, used for the analysis described in the manuscript. The content of the folder is further described in a README text file. For more information or additional data/code, please contact the authors. Additional code can be found on Github: https://github.com/moser297/fMEG_data_processing/tree/main/Code_DCN_2020_Neo.
DOI: 10.5281/zenodo.4018827
title: Data for "Magnetoencephalographic Signatures of Hierarchical Rule Learning in Newborns"

description: Data uploaded here belongs to the manuscript "Magnetoencephalographic Signatures of Hierarchical Rule Learning in Newborns" (Moser, J., Schleger, F., Weiss, M., Sippel, K., Dehaene-Lambertz, G., & Preissl, H. (2020). Magnetoencephalographic signatures of hierarchical rule learning in newborns. Developmental cognitive neuroscience, 46, 100871). It is processed MEG data and an R script, used for the analysis described in the manuscript. The content of the folder is further described in a README text file. For more information or additional data/code, please contact the authors. Additional code can be found on Github: https://github.com/moser297/fMEG_data_processing/tree/main/Code_DCN_2020_Neo.
DOI: 10.5281/zenodo.10303537
title: Ascending and Descending Geocoded Gamma Interferograms for Detecting co-seismic deformation in Costa Rica using Sentinel-1 InSAR

description: A complete description of the SLC images used for these interferograms is in Table 2 of the article "Towards a geodetic earthquake catalogue for Central America: Detecting co-seismic deformation in Costa Rica using Sentinel-1 InSAR"
DOI: 10.5281/zenodo.10303538
title: Ascending and Descending Geocoded Gamma Interferograms for Detecting co-seismic deformation in Costa Rica using Sentinel-1 InSAR

description: A complete description of the SLC images used for these interferograms is in Table 2 of the article "Towards a geodetic earthquake catalogue for Central America: Detecting co-seismic deformation in Costa Rica using Sentinel-1 InSAR"
DOI: 10.17909/gg9m-3065
title: TESS Data Validation Files: Multi-Sector 14-50

description: All data validation files from Multi-Sector 14-50.
DOI: 10.17909/bkt9-2f16
title: TESS Data Validation Files: Multi-Sector 42-46

description: All data validation files from Multi-Sector 42-46.
DOI: 10.17909/qvte-5055
title: TESS Data Validation Files: Multi-Sector 1-46

description: All data validation files from Multi-Sector 1-46.
DOI: 10.17909/q9h7-1w66
title: TESS Data Validation Files: Multi-Sector 42-43

description: All data validation files from Multi-Sector 42-43.
DOI: 10.17909/w9hg-ge53
title: TESS Data Validation Files: Multi-Sector 14-41

description: All data validation files from Multi-Sector 14-41.
DOI: 10.17909/ngf4-bp43
title: TESS Data Validation Files: Multi-Sector 1-39

description: All data validation files from Multi-Sector 1-39.
DOI: 10.17909/59zv-e384
title: TESS Data Validation Files: Multi-Sector 1-36

description: All data validation files from Multi-Sector 1-36.
DOI: 10.17909/9vtx-6h13
title: TESS Data Validation Files: Multi-Sector 14-26

description: All data validation files from Multi-Sector 14-26.
DOI: 10.17909/2rrz-sa21
title: TESS Data Validation Files: Multi-Sector 14-23

description: All data validation files from Multi-Sector 14-23.
DOI: 10.17909/1k0h-yq73
title: TESS Data Validation Files: Multi-Sector 14-19

description: All data validation files from Multi-Sector 14-19.
DOI: 10.17909/y4ef-z309
title: TESS Data Validation Files: Multi-Sector 14-16

description: All data validation files from Multi-Sector 14-16.
DOI: 10.17909/3852-b802
title: TESS Data Validation Files: Multi-Sector 1-13

description: All data validation files from Multi-Sector 1-13.
DOI: 10.17909/k3rx-v275
title: TESS Data Validation Files: Multi-Sector 1-9

description: All data validation files from Multi-Sector 1-9.
DOI: 10.17909/kjax-n504
title: TESS Data Validation Files: Multi-Sector 1-6

description: All data validation files from Multi-Sector 1-6.
DOI: 10.5281/zenodo.10202189
title: Optimizing Volume Estimation in Aquaculture Sea Cages: A Comparative Study of Calculation Methods and Sensor Placement

description: Software and data for calculation of volume reduction in aquaculture cages of gravity type
DOI: 10.5281/zenodo.10202190
title: Optimizing Volume Estimation in Aquaculture Sea Cages: A Comparative Study of Calculation Methods and Sensor Placement

description: Software and data for calculation of volume reduction in aquaculture cages of gravity type
DOI: 10.6084/m9.figshare.24251884
title: Modeling atmospheric transport of cosmogenic radionuclide ¹⁰Be using GEOS-Chem 14.1.1 and ECHAM6.3-HAM2.3: implications for solar and geomagnetic reconstructions<b> </b>

description: Model outputs for the paper "Modeling atmospheric transport of cosmogenic radionuclide ¹⁰Be using GEOS-Chem 14.1.1 and ECHAM6.3-HAM2.3: implications for solar and geomagnetic reconstructions"<br>It includes the model outputs from the ECHAM6.3-HAM2.3 ("E63H23" folder) and GEOS-Chem 14.1.1 ("GEOS-Chem" folder) using the Beryllium production model by Poluianov et al., (2016).The model outputs are averaged over the period 2005-2013. Both "E63H23" and "GEOS-Chem" folders have the following subfolders- "CONTROL" folder: simulations with production rates using Phi from the Herbst et al., (2017) and spatial geomagnetic cut-off rigidity from Copeland (2018).- "Solar_minimum_200MeV" folder: simulations with production rates using constant Phi=200 MeV and spatial geomagnetic cut-off rigidity from Copeland (2018).- "Geomagnetic_minium_Laschamps" folder: simulations with production rates using constant Phi=500 MeV and spatial geomagnetic cut-off rigidity from generated by the geomagnetic field model LSMOD.2 at the period 40.95 ka (Gao et al., 2022).
DOI: 10.6084/m9.figshare.24251884.v1
title: Modeling atmospheric transport of cosmogenic radionuclide ¹⁰Be using GEOS-Chem 14.1.1 and ECHAM6.3-HAM2.3: implications for solar and geomagnetic reconstructions<b> </b>

description: Model outputs for the paper "Modeling atmospheric transport of cosmogenic radionuclide ¹⁰Be using GEOS-Chem 14.1.1 and ECHAM6.3-HAM2.3: implications for solar and geomagnetic reconstructions"<br>It includes the model outputs from the ECHAM6.3-HAM2.3 ("E63H23" folder) and GEOS-Chem 14.1.1 ("GEOS-Chem" folder) using the Beryllium production model by Poluianov et al., (2016).The model outputs are averaged over the period 2005-2013. Both "E63H23" and "GEOS-Chem" folders have the following subfolders- "CONTROL" folder: simulations with production rates using Phi from the Herbst et al., (2017) and spatial geomagnetic cut-off rigidity from Copeland (2018).- "Solar_minimum_200MeV" folder: simulations with production rates using constant Phi=200 MeV and spatial geomagnetic cut-off rigidity from Copeland (2018).- "Geomagnetic_minium_Laschamps" folder: simulations with production rates using constant Phi=500 MeV and spatial geomagnetic cut-off rigidity from generated by the geomagnetic field model LSMOD.2 at the period 40.95 ka (Gao et al., 2022).
DOI: 10.5066/p9uhe6pg
title: Monitoring data to support the operation of the Electric Dispersal Barrier System on the Chicago Sanitary and Ship Canal at Romeoville, Illinois, October 1, 2017, to September 30, 2018

description: The U.S. Geological Survey monitors water surface flow reversals, commercial vessel traffic, and temperature and specific conductance in the U.S. Army Corps of Engineers Electric Dispersal Barrier System (EDBS) on the Chicago Sanitary and Ship Canal, Chicago, Illinois. This data release is the 2018 water year summary of the EDBS monitoring data. Water surface flow reversals at the EDBS are monitored using surface velocity radar. Commercial vessel traffic patterns through the EDBS are documented using a motion-activated video camera. Temperature and specific conductance are reported in near real-time from USGS sensors installed just downstream of the EDBS.