cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28vt32
title: CCDC 2111134: Experimental Crystal Structure Determination

description: Related Article: Qiaoqiao Jia, Jeremy P. Krogman|2021|Organometallics|40|3958|doi:10.1021/acs.organomet.1c00537
DOI: 10.5517/ccdc.csd.cc28vt43
title: CCDC 2111135: Experimental Crystal Structure Determination

description: Related Article: Qiaoqiao Jia, Jeremy P. Krogman|2021|Organometallics|40|3958|doi:10.1021/acs.organomet.1c00537
DOI: 10.24431/rw1k5bf
title: Conductivity, temperature, and depth data from CTDs deployed on mooring N2 for the ASGARD Project, Bering Sea, 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). Two moorings were recovered by dragging (N1 and N5).
DOI: 10.5517/ccdc.csd.cc28vt54
title: CCDC 2111136: Experimental Crystal Structure Determination

description: Related Article: Qiaoqiao Jia, Jeremy P. Krogman|2021|Organometallics|40|3958|doi:10.1021/acs.organomet.1c00537
DOI: 10.5517/ccdc.csd.cc28vt65
title: CCDC 2111137: Experimental Crystal Structure Determination

description: Related Article: Qiaoqiao Jia, Jeremy P. Krogman|2021|Organometallics|40|3958|doi:10.1021/acs.organomet.1c00537
DOI: 10.24431/rw1k5bg
title: Conductivity, temperature, and depth data from CTDs deployed on mooring N3 for the ASGARD Project, Bering Sea, 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). Two moorings were recovered by dragging (N1 and N5).
DOI: 10.24431/rw1k5bh
title: Conductivity, temperature, and depth data from CTDs deployed on mooring N4 for the ASGARD Project, Bering Sea, 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). Two moorings were recovered by dragging (N1 and N5).
DOI: 10.24431/rw1k5bi
title: Conductivity, temperature, and depth data from CTDs deployed on mooring N5 for the ASGARD Project, Bering Sea, 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). Two moorings were recovered by dragging (N1 and N5).
DOI: 10.24431/rw1k5bj
title: Conductivity, temperature, and depth data from CTDs deployed on mooring N6 for the ASGARD Project, Bering Sea, 2017-2019

description: These data are part of the Arctic Shelf Growth, Advection, Respiration and Deposition (ASGARD) Rate Measurements project, funded as part of the North Pacific Research Board’s (NPRB’s) Arctic Integrated Ecosystem Research Program (IERP; http://www.nprb.org/arctic- program/). The program and research on this expedition was sponsored by NPRB, the Collaborative Alaskan Arctic Studies Program (CAASP, formerly the North Slope Borough/Shell Baseline Studies Program), the Bureau of Ocean Energy Management (BOEM), the National Science Foundation, and the Office of Naval Research Marine Mammals and Biology Program. In-kind support for this research cruise was contributed by the National Oceanic and Atmospheric Administration and the University of Alaska Fairbanks. For this study, 6 moorings were deployed in the northern Bering Sea and southern Chukchi Sea. Each mooring was equipped with a variety of instruments to provide time series of environmental conditions throughout the water column. Moorings were deployed during the R/V Sikuliaq cruise SKQ201709S in June 2017. With one exception, they were recovered during the R/V Sikuliaq cruise SKQ201813S in June 2018. One mooring, N5, was unable to be recovered because of problems with the mooring release, and was recovered by a small-boat operation staged out of Kivalina, AK, in July 2018. In the second year of the project, 6 mooring were re-deployed at the same locations during R/V Sikuliaq cruise SKQ201813S. Moorings were recovered during the R/V Ocean Starr (https://www.stabbertmaritime.com/vessels-2/ocean-starr/) ArcticIES Phase II Cruise, Leg 1 (no cruise id?). Two moorings were recovered by dragging (N1 and N5).
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 136.1, 132.1, 130.4, 120.1, 115.6, 28.4.
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 161.8 (C=O), 154.5 (COH), 152.0 (Cq), 143.5 (Cq), 136.1 (CHar), 132.1 (CHar), 130.4 (CHar, 2C), 130.1 (Cq), 128.3 (Cq), 123.3 (Cq), 120.1 (CH), 116.7 (Cq), 115.6 (CHar, 2C), 36.1 (CH2), 28.4 (CH(CH3)2), 23.5 (CH(CH3)2, 2C), 15.4 (CH3).
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0001150
title: 1H--1H correlation spectroscopy (1H-1H COSY) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for 1H--1H correlation spectroscopy (1H-1H COSY)
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0002337
title: electrospray ionisation quadrupole detection (ESI Q Trap) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for electrospray ionisation quadrupole detection (ESI Q Trap) MS (ESI), m/z (%): 310.2 (21), 309.1 (100) [M+H]+, 297.1 (10), 282.3 (22), 223.1 (25), 145.1 (12), 131.1 (11), 100.1 (25).HR-MS (ESI), m/z C20H21O3 (%): calc 309.1485, found 309.1480 (100).
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0001075
title: elemental analysis (EA) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for elemental analysis (EA)
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3272 (m), 3183 (w), 3064 (w), 2963 (w), 2927 (w), 2894 (w), 2874 (w), 2827 (w), 1681 (vs), 1612 (m), 1598 (s), 1574 (w), 1517 (s), 1485 (m), 1462 (w), 1442 (m), 1422 (w), 1383 (w), 1363 (m), 1347 (w), 1310 (w), 1295 (w), 1264 (m), 1237 (s), 1231 (s), 1208 (m), 1184 (s), 1170 (s), 1101 (w), 1085 (s), 1052 (w), 1038 (w), 1000 (m), 970 (w), 952 (w), 899 (w), 888 (w), 856 (m), 849 (w), 834 (m), 827 (s), 820 (m), 786 (m), 775 (w), 741 (w), 722 (w), 710 (w), 691 (w), 670 (m), 654 (m), 633 (m), 555 (m), 540 (m), 487 (m), 459 (w), 441 (w), 428 (w), 392 (w) cm–1.
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 7.64 (s, 1H), 7.28–7.27 (m, 1H), 7.19–7.16 (m, 2H), 7.07 (d, J = 7.8 Hz, 1H), 6.84–6.81 (m, 2H), 5.04 (s, 1H), 3.86 (s, 2H), 3.21 (sept, J = 7.0 Hz, 1H), 2.41 (s, 3H), 1.24 (d, J = 6.8 Hz, 6H).
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 136.1 (+), 132.1 (+), 130.4 (+), 120.1 (+), 115.6 (+), 36.1 (–), 28.4 (+), 23.6 (+), 15.4 (+).
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n/chmo0001148
title: 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC) ()
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: dataset for 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC)
DOI: 10.14272/vfjcnecvclzjmx-uhfffaoysa-n.1
title: 3-(4-hydroxybenzyl)-5-isopropyl-8-methyl-2H-chromen-2-one
title: InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3

description: This dataset contains experimental data from a sample (InChI=1S/C20H20O3/c1-12(2)17-9-4-13(3)19-18(17)11-15(20(22)23-19)10-14-5-7-16(21)8-6-14/h4-9,11-12,21H,10H2,1-3H3). The datasets from the following experiments are available: infrared absorption spectroscopy (IR) 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT) 1H--1H correlation spectroscopy (1H-1H COSY) 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) 1H--13C heteronuclear multiple bond coherence (13C-1H HMBC) electrospray ionisation quadrupole detection (ESI Q Trap) elemental analysis (EA)
DOI: 10.5281/zenodo.5789436
title: An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes

description: The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).
DOI: 10.5281/zenodo.5789435
title: An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes

description: The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).
DOI: 10.3886/e119548
title: Data and Code for Endogenous Separations, Wage Rigidities and Unemployment Volatility

description: We show that in microdata, as well as in a search and matching model with flexible wages for new hires, wage rigidities of incumbent workers have substantial effects on separations and unemployment volatility. Allowing for an empirically relevant degree of wage rigidities for incumbent workers drives unemployment volatility, as well as the volatility of vacancies and tightness to that in the data. Thus, the degree of wage rigidity for newly hired workers is not a sufficient statistic for determining the effect of wage rigidities on macroeconomic outcomes. This finding affects the interpretation of a large empirical literature on wage rigidities.
DOI: 10.3886/e119548v1
title: Data and Code for Endogenous Separations, Wage Rigidities and Unemployment Volatility

description: We show that in microdata, as well as in a search and matching model with flexible wages for new hires, wage rigidities of incumbent workers have substantial effects on separations and unemployment volatility. Allowing for an empirically relevant degree of wage rigidities for incumbent workers drives unemployment volatility, as well as the volatility of vacancies and tightness to that in the data. Thus, the degree of wage rigidity for newly hired workers is not a sufficient statistic for determining the effect of wage rigidities on macroeconomic outcomes. This finding affects the interpretation of a large empirical literature on wage rigidities.
DOI: 10.3886/e156501
title: Comparing the Growth and Predictive Performance of a Traditional Oral Reading Fluency Measure to an Experimental Novel Measure

description: Curriculum-based measurement of oral reading fluency (CBM-R) is used as an indicator of reading proficiency, and to measure at risk students’ response to reading interventions to help ensure effective instruction. The purpose of this study was to compare model-based WCPM scores (CORE) to Traditional CBM-R WCPM scores to determine which provides more reliable growth estimates and demonstrates better predictive performance of reading comprehension and state reading test scores. Results indicated that in general, CORE had better (a) within-growth properties (smaller SDs of slope estimates and higher reliability), and (b) predictive performance (lower RMSE, and higher R-squared, sensitivity, specificity, and AUC values). These results suggest increased measurement precision for the model-based CORE scores compared to Traditional CBM-R, providing preliminary evidence that CORE can be used for consequential assessment.<br>
DOI: 10.3886/e156501v2
title: Comparing the Growth and Predictive Performance of a Traditional Oral Reading Fluency Measure to an Experimental Novel Measure

description: Curriculum-based measurement of oral reading fluency (CBM-R) is used as an indicator of reading proficiency, and to measure at risk students’ response to reading interventions to help ensure effective instruction. The purpose of this study was to compare model-based WCPM scores (CORE) to Traditional CBM-R WCPM scores to determine which provides more reliable growth estimates and demonstrates better predictive performance of reading comprehension and state reading test scores. Results indicated that in general, CORE had better (a) within-growth properties (smaller SDs of slope estimates and higher reliability), and (b) predictive performance (lower RMSE, and higher R-squared, sensitivity, specificity, and AUC values). These results suggest increased measurement precision for the model-based CORE scores compared to Traditional CBM-R, providing preliminary evidence that CORE can be used for consequential assessment.<br>
DOI: 10.6084/m9.figshare.14885529
title: A Retrospective Cohort Study of Healthcare Utilization Associated with Paravertebral Blocks for Chronic Pain Management in Ontario

description: <b>Background</b>: Injections, particularly paravertebral blocks (PVBs), are frequently performed procedures in Ontario, Canada, for the management of chronic pain, despite limited evidence and risk of complications. <b>Aim:</b> This study examines usage patterns of PVBs to evaluate their effects on healthcare utilization and opioid prescribing. <b>Methods:</b> A retrospective cohort study in Ontario using administrative data. Ontario residents receiving their initial PVBs between July 1, 2013 and March 31, 2018 were included. Changes in use of other interventions, physician visits, and opioids were compared to the 12-month periods before and after index PVBs. Data use was authorized under section 45 of Ontario’s Personal Health Information Protection Act. <b>Results:</b> 47,723 patients received their initial PVBs in the study period. The rate of index PVBs increased from 1.61 per 10,000 population (2013) to 2.26 per 10,000 (2018). Initial PVBs were performed most commonly by family physicians (<i>N</i> = 25,042), followed by anesthesiologists (<i>N</i> = 14,195). 23,386 patients (49%) received 1 to 9 repeat PVBs in the 12 months after index PVB; 12,474 patients (26.15%) received 10 or more. Use of other nonimage guided interventional pain procedures per patient (mean±SD) increased from 2.19 ± 9.35 to 31.68 ± 52.26 in the year before and after index PVB. Relevant physician visits per patient (mean±SD) also increased from 2.92 ± 3.61 to 9.64 ± 11.77. Mean opioid dosing did not change significantly between the year before and the year after index PVB. <b>Conclusion:</b> PVBs are associated with increases in healthcare utilization and no change in opioid use patterns.