cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p9kakmab
title: Bathymetric data for the Maumee River between Defiance and Toledo, Ohio, 2019

description: As part of the Great Lakes Restoration Initiative (GLRI) project template 774-18 entitled "Development of monitoring and response methodologies, and implementation of an Adaptive Management Framework to work towards Eradication of Grass Carp in Lake Erie" an integrated bathymetric/hydrodynamic/water-quality survey of the Maumee River (Ohio) was completed by the U.S. Geological Survey (USGS) in the summer of 2019. These data were collected to inform the development of a one-dimensional hydraulic model and associated Fluvial Egg Drift Simulator (FluEgg) model of the Maumee River downstream from Defiance, Ohio. The data contained in this data release were collected by the USGS Ohio-Kentucky-Indiana Water Science Center to inform the development of these models by the USGS Central Midwest Water Science Center. The survey was completed over two periods of time: June 24-28, 2019, and July 29 to August 1, 2019. The first survey period concentrated on the reach between Waterville, Ohio, and Lake Erie, while the second period concentrated on the reach between Defiance, Ohio, and Grand Rapids, Ohio. Survey data include bathymetry (riverbed elevation), three-dimensional water velocity, discharge, and basic water-quality parameters. However, this data release is limited to only the bathymetry data (riverbed elevations) from the survey. A total of 251 cross sections were surveyed (141 upstream from and 110 downstream from Grand Rapids Dam, respectively) and data were also collected along streamwise transits between sections. Due to rapids, high-water, access, and safety concerns, no data were collected in the 23.9 kilometer reach downstream from the dam at Grand Rapids, Ohio. The upstream-most cross section is 280 meters downstream from the low-head dam approximately 6.6 kilometers downstream from Defiance, Ohio. The downstream-most cross section is located 290 meters downstream from the U.S. Coast Guard Station at Toledo, Ohio (3900 N Summit St, Toledo, OH 43611). All data were collected by a manned survey vessel with a two-person survey crew of trained hydrographers. All data were georeferenced using a Trimble R10 Global Navigation Satellite System (GNSS) receiver mounted on the survey vessel and connected to the Ohio Department of Transportation (ODOT) real-time virtual reference station (VRS) network. Depths were measured using the 600 kHz vertical beam from a 1200 kHz Teledyne RD Instruments RiverPro acoustic Doppler current profiler (ADCP) deployed on a fixed mount from the survey vessel. The GNSS receiver was mounted directly above the ADCP and the offset between the GNSS antenna and the face of the vertical beam transducer was measured in the field and accounted for in post-processing. The sampling frequency varied slightly with the dynamic configuration of the ADCP but was generally between 1 to 2 Hertz.
DOI: 10.6073/pasta/c705a3cf1492716c7ac19d7cd1423b55
title: Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2011 through 31-Dec-2011

description: Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2011 through 31-Dec-2011. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
DOI: 10.6073/pasta/73b48d6008a86509291b97e513307b85
title: Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2012 through 31-Dec-2012

description: Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2012 through 31-Dec-2012. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
DOI: 10.6073/pasta/1b0236a552de365151163eb530299469
title: Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2014 through 31-Dec-2014

description: Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2014 through 31-Dec-2014. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
DOI: 10.6073/pasta/9f24a43f1d02b3f80c9cff2dfc9e33e5
title: Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2013 through 31-Dec-2013

description: Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2013 through 31-Dec-2013. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
DOI: 10.6073/pasta/d56585f4793c93a37669d13a916b0437
title: Lapland longspur and Gambel's white crowned sparrow egg and nestling survival near Toolik Field Station, Alaska, summers 2012-2016

description: This data set contains information about the daily status (alive/ dead) of Lapland longspur and Gambel's white-crowned sparrow eggs and nestlings studied near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
DOI: 10.6073/pasta/914dcfb57594358f2403d56ec3efe002
title: Precense/ absence of new snow-fall scored from time-lapse photography collected near Toolik Field Station, Alaska, summers 2012-2016

description: This data set describes the presence/ absence of new snowfall approximated daily using time -lapse photography images near Toolik Field Station during summers from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). Additional cameras funded by other grants were also used for scoring including multiple Toolik EDC timelapse images taken at Toolik, Atigun Ridge, and Imnavait. Additional data were scored from time lapse photography taken by the Deegan and Urban labs (Office of Polar Programs #0902153 and #1417664). All data are associated with publication DOI: 10.1111/jav.01712.
DOI: 10.6073/pasta/7368b2e1928127bdf51b9ed7d87e7f52
title: Hourly meteorological data gapfilled for sensor downtimes collected near Toolik Field Station, Alaska, summers 2012-2016

description: This data set includes meteorological parameters collected near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It also includes meteorological data collected by two additional entities that are available on public repositories. Toolik data reflect data collected by the Toolik Envronmental Data Center and Imnavait data reflect data collected by the Arctic Observatory Network (AON). These data have been modified such that that sensor downtimes have been gapfilled by pulling data from the next nearest station. These data are associated with publication DOI: 10.1111/jav.01712
DOI: 10.6073/pasta/2a68a3a7e72d175426edf5cae7904062
title: Arthropod pitfall trap biomass captured (weekly) and pitfall biomass model predictions (daily) near Toolik Field Station, Alaska, summers 2012-2016.

description: This data set contains information about the per pitfall trap arthropod biomass captured (or modeled using GAM modelling approaches) near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
DOI: 10.6073/pasta/db8f3c58a0ace1151150d5497c2f1fab
title: Arthropod biomass captured by sweepnet (weekly) and sweepnet biomass model predictions (daily) near Toolik Field Station, Alaska, summers 2012-2016

description: This data set contains information about the per sample sweepnet arthropod biomass captured (or modeled using GAM modelling approaches) near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
DOI: 10.3886/e158082v1
title: School district Twitter use

description: While educators’ uses of social media for purposes such as professional learning and networking are now well-established, our understanding of how educational institutions use social media—including to engage key stakeholders during periods of crisis—is limited. In this study, we used a public data mining research approach to examine how K-12 school districts in the United States used Twitter as a communication tool during a critical period of the COVID-19 pandemic, March-April, 2020. Through a three-step grounded theory approach of 1,357 district tweets from 492 school districts, we found that districts worked to build community and share time-sensitive announcements in alignment with social media crisis communication recommendations. Announcements were more common during the early stages of the pandemic (and were engaged more collaboratively), with community-building posts more common later on. This study demonstrates how researchers can use publicly available (social media) data to understand districts’ communication priorities and strategies during and beyond periods of crisis.
DOI: 10.6073/pasta/8cf05d3ba01b54b6748b5b87caa4c505
title: Contribution of CO2 and CH4 emissions at ice-melt to annual emissions from 450 and 270 lakes, respectively, 1986 to 2014

description: The ice-covered period on lakes in the northern hemisphere can be extensive, lasting up to 7 months of the year. During this time, C cycling in lakes is altered affecting CO2 and CH4 dynamics below ice. Lake ice impedes atmospheric exchange, trapping CO2 and CH4 in the lake over winter. As lake ice-melts, CO2 and CH4 that has accumulated over winter is emitted from the into the atmosphere. To investigate the importance of CO2 and CH4 emissions during the ice-melt period, we conducted a literature search for studies that had CO2 and CH4 emission estimates for both the ice-melt and open water period. From these literature values, we could calculate the percent contribution of the ice-melt period to annual CO2 and CH4 emissions. We obtained data for 271 (n= 258) and 447 (n= 689) individual lakes, for CH4 and CO2, respectively.
DOI: 10.6073/pasta/98855475790bfb012066f9dc802ecc31
title: MCR LTER: Coral Reef: Long-Term Coral Population and Community Dynamics: Annual Island Wide Coral Demography Survey 2011 to present

description: Demographic performance (recruitment, growth, and survival) are quantified annually for multiple individual colonies of the three most common genera (Acropora, Pocillopora, Porites) at both backreef and forereef sites. Each coral was tagged in 2011 and subsequently sampled annually to track colony growth and mortality dynamics. Since 2013, investigators have transitioned to identifying coral through detailed mapping methodology and will continue to identify corals using this method in the subsequent years.
DOI: 10.6073/pasta/18178cf49b8e939b39e9ae5901b2b249
title: MCR LTER: Coral Reefs: Coral recruitment to 25 m2 plots on the forereef surrounding Moorea, French Polynesia, 2011-2015

description: These data report the number of Pocilloporid, Acroporid and Poritiid corals recruiting annually to permament 5 m X 5 m plots established at a depth of approximately 10m on the forereef of Moorea, French Polynesia following an outbreak (2007-2010) of the corallivorous crown-of-thorns seastars (Acanthaster planci) and the close passage of Cyclone Oli to Moorea in February, 2010. These two perturbations resulted in a significant loss of live coral coral from the fore reef on an island-wide scale (crown-of-thorns) and a loss of habitat structural complexity from fore habitats on the Moorea's north shore (Cyclone Oli). Adam, T.C. et al. 2011 "Herbivory, connectivity and ecosystem resilience: response of a coral reef to a large-scale perturbation" PLoS One e23717 for a more complete discription of the system, the nature and magnitudes of the perturbations and the short-term response by the coral community to the perturbations. Data were obtained by counting all Pocilloporid, Acroporid and Poritiid coral recruits &lt; 3 cm in colony diameter observed within 25 5 m x 5 m plots established at four sites in a depth of approximately 10m on the forereef of Moorea, French Polynesia. Two sites, Resilience 1 (R1) containing 10 5 m x 5 m plots and Resilence 2 (R2) containing five 5 m x 5 m plots were established along the north shore of the island. Two additional sites, Resilience 4 (R4) and Resilience 5 (R5), each containing five 5 m x 5 m plots were established on the southeast and southwest shores of the island, respectively. Plots were permanently marked using stainless steel bolts cemented into the reef matrix so that recruit counts could be made repeatedly within the same plots on an annual basis. This dataset knb-lter-mcr.5023 is a subset of a larger dataset (knb-lter-mcr.7008) produced to accompany a paper submission. The parent dataset, knb-lter-mcr.7008, also contains data on the number of corals recruiting to an additional two sites (Resilience 3 and Resilience 6) established on the fore reef surrounding Moorea, French Polynesia, estimates of the size distribution of Pocilloporid, Acroporid and Poritiid corals occuring within each plot, the abundances and biomasses of fishes within each plot, the abundances of several species of macro-invertebrates observed within each of the 5 m x 5 m plots, as well as coarse scale measurements of the surface rugosity within each plot.
DOI: 10.26207/h7cq-yb97
title: Pesticide Seed Coatings and Cover Crops
DOI: 10.34740/kaggle/dsv/3017257
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.14776128.v2
title: Data.rar

description: This is a high-resolution gridded LULC dataset in China under a full SSP/RCP matrix from 2010 to 2100, where the urban land change is well incorporated.
DOI: 10.17632/3yxkps6msr.1
title: Persistent B-cell memory after SARS-CoV-2 vaccination is functional during breakthrough infections.

description: We reported the R code used to study the relationship between variables using a simple linear regression model in the software R (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Accessed 24/09/2021).
DOI: 10.7298/eh5g-yf61
title: Supplementary Data from: Responsible antibiotic use labeling and consumers’ willingness to buy and pay for fluid milk
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000739
title: 31P nuclear magnetic resonance spectroscopy (31P NMR) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for 31P nuclear magnetic resonance spectroscopy (31P NMR) 31P NMR (202 MHz, DMSO-d6, ppm) δ = 12.47 (s).
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (126 MHz, DMSO-d6, ppm) δ = 141.7 (d, J = 3.1 Hz, 2C, Cq, CAr), 133.6 (d, J = 181.9 Hz, 2C, Cq, CAr), 131.2 (+, d, J = 10.0 Hz, 4C, CH, CAr), 126.6 (+, d, J = 14.4 Hz, 4C, CH, CAr).
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2721 (w), 1602 (w), 1387 (w), 1145 (m), 1098 (s), 1007 (vs), 997 (vs), 942 (vs), 853 (w), 833 (m), 815 (vs), 711 (vs), 545 (vs), 520 (vs), 460 (vs), 405 (m), 390 (m) cm–1.
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n.1
title: [4-(4-phosphonophenyl)phenyl]phosphonic acid
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: This dataset contains experimental data from a sample ([4-(4-phosphonophenyl)phenyl]phosphonic acid, InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) 31P nuclear magnetic resonance spectroscopy (31P NMR) distortionless enhancement with polarization transfer (DEPT) electrospray ionisation mass spectrometry (ESMS) infrared absorption spectroscopy (IR)
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000482
title: electrospray ionisation mass spectrometry (ESMS) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for electrospray ionisation mass spectrometry (ESMS) HRMS (ESI, [M – H]-, C6H7O6P2) calcd.: 313.0036, found: 313.0036.
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (500 MHz, DMSO-d6, ppm) δ = 7.83–7.72 (m, 8H, HAr).(4H of PO(OH)2 not visible)
DOI: 10.14272/qwszmkcgmdroke-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) ([4-(4-phosphonophenyl)phenyl]phosphonic acid)
title: InChI=1S/C12H12O6P2/c13-19(14,15)11-5-1-9(2-6-11)10-3-7-12(8-4-10)20(16,17)18/h1-8H,(H2,13,14,15)(H2,16,17,18)

description: dataset for distortionless enhancement with polarization transfer (DEPT)
DOI: 10.6084/m9.figshare.13333769
title: A Two-Layer Turbulence-based Model to Predict Suspended Sediment Concentration in Flows with Aquatic Vegetation

description: Data presented and discussed on "A Two-Layer Turbulence-based Model to Predict Suspended Sediment Concentration in Flows with Aquatic Vegetation", by Chien-Yung Tseng and Rafael O. Tinoco, published in Geophysical Research Letters (2021). Data files are described in details in the Data Description File.
DOI: 10.6084/m9.figshare.13333769.v1
title: A Two-Layer Turbulence-based Model to Predict Suspended Sediment Concentration in Flows with Aquatic Vegetation

description: Data presented and discussed on "A Two-Layer Turbulence-based Model to Predict Suspended Sediment Concentration in Flows with Aquatic Vegetation", by Chien-Yung Tseng and Rafael O. Tinoco, published in Geophysical Research Letters (2021). Data files are described in details in the Data Description File.
DOI: 10.6084/m9.figshare.12486590
title: A model to predict surface gas transfer rate in streams based on turbulence production by aquatic vegetation

description: Data presented and discussed on "A model to predict surface gas transfer rate in streams based on turbulence production by aquatic vegetation", by Chien-Yung Tseng and Rafael O. Tinoco, published in Advances in Water Resources (2020). Data files are described in details in the Data Description File.
DOI: 10.6084/m9.figshare.12486590.v2
title: A model to predict surface gas transfer rate in streams based on turbulence production by aquatic vegetation

description: Data presented and discussed on "A model to predict surface gas transfer rate in streams based on turbulence production by aquatic vegetation", by Chien-Yung Tseng and Rafael O. Tinoco, published in Advances in Water Resources. Data files are described in detail in the Data Description File.
DOI: 10.14272/jvgpcxscdwsvru-hwnmuzrgsa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C76H47N9.Ni.Zn/c1-7-19-47(20-8-1)69-53-31-35-57(77-53)71(49-23-11-3-12-24-49)61-39-43-65(81-61)75(66-44-40-62(82-66)72(50-25-13-4-14-26-50)58-36-32-54(69)78-58)85-76-67-45-41-63(83-67)73(51-27-15-5-16-28-51)59-37-33-55(79-59)70(48-21-9-2-10-22-48)56-34-38-60(80-56)74(52-29-17-6-18-30-52)64-42-46-68(76)84-64;;/h1-46,85H;;/q-4;2*+2/b69-53-,69-54-,70-55-,70-56-,71-57-,71-61-,72-58-,72-62-,73-59-,73-63-,74-60-,74-64-,75-65+,75-66+,76-67+,76-68+;;

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3053 (w), 2915 (w), 2853 (w), 1545 (vw), 1485 (w), 1465 (w), 1441 (w), 1405 (w), 1334 (vw), 1128 (m), 1069 (s), 1048 (s), 1035 (s), 1003 (vs), 979 (s), 959 (s), 953 (s), 790 (m), 752 (m), 742 (m), 718 (m), 700 (s), 449 (vs), 435 (vs), 429 (vs), 411 (vs), 399 (vs), 382 (vs) cm–1.
DOI: 10.14272/jvgpcxscdwsvru-hwnmuzrgsa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C76H47N9.Ni.Zn/c1-7-19-47(20-8-1)69-53-31-35-57(77-53)71(49-23-11-3-12-24-49)61-39-43-65(81-61)75(66-44-40-62(82-66)72(50-25-13-4-14-26-50)58-36-32-54(69)78-58)85-76-67-45-41-63(83-67)73(51-27-15-5-16-28-51)59-37-33-55(79-59)70(48-21-9-2-10-22-48)56-34-38-60(80-56)74(52-29-17-6-18-30-52)64-42-46-68(76)84-64;;/h1-46,85H;;/q-4;2*+2/b69-53-,69-54-,70-55-,70-56-,71-57-,71-61-,72-58-,72-62-,73-59-,73-63-,74-60-,74-64-,75-65+,75-66+,76-67+,76-68+;;

description: dataset for mass spectrometry (MS) HRMS - ESI (C76H47N9NiZn): calcd 1207.2599, found 1207.2544.