cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34943/c9695530-84db-4f7b-991b-4c41ddec0b60
title: Port Alberni Meteorological Station Deployed 2012-01-06

description: The Davis Vantage Pro2 Plus 6162W was deployed on 2012-01-06 at Port Alberni. Port Alberni is a city located on Vancouver Island. This device is a Meteorological Station. Meteorological stations are instruments that make routine meteorological measurements on the atmosphere and are comprised of a suite of sensors. Typical examples of measurements include temperature, barometric pressure, humidity, rainfall, solar radiation, wind speed and direction. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.21223/kqlro5/jalogx
title: 01_Data_Peru_Temperatura_Polillas_gorgojos.tab
DOI: 10.21223/kqlro5/ttwc7v
title: 02_Data_Peru_capturas_polillas_gorgojos.tab
DOI: 10.21223/kqlro5
title: Dataset for: Datos de capturas de complejo polilla y Gorgojo de los Andes en Perú

description: Capturas de complejo polilla y Gorgojo de los Andes en diferentes zonas de Peru
DOI: 10.5066/p914el20
title: Lake Erie Fish Community Data, 2013-2021

description: Assessing the distribution and abundance of both predator and prey (forage) fish species is a cornerstone of ecosystem-based fishery management, and supports decision making that considers food-web interactions. In support of binational Great Lakes fishery management the objectives of this survey were to: provide estimates of densities of key forage and predator species in the western basin of Lake Erie, to assess seasonal and spatial distributions of fishes in tandem with water quality information, and to assess year class strength. A systematic grid sampling approach with 41 stations was sampled via bottom trawl during June (Spring) and September (Autumn), starting in 2013. This data release adds 2021 data to the set for a total of nine years observation using the same gear and sampling design.
DOI: 10.18119/n9zc89
title: Orexin Receptor 1 Network
DOI: 10.23641/asha.19449839
title: Vocabulary, diagnosis, and intervention in DHH infants/toddlers (Campbell & Bergelson, 2022)

description: <b>Purpose: </b>This study sought to (a) characterize the demographic, audiological, and intervention variability in a population of Deaf and Hard of Hearing (DHH) children receiving state services for hearing loss; (b) identify predictors of vocabulary delays; and (c) evaluate factors influencing the success and timing of early identification and intervention efforts at a state level.<b>Method: </b>One hundred DHH infants and toddlers (aged 4–36 months) enrolled in early intervention completed the MacArthur–Bates Communicative Development Inventories, and detailed information about their audiological and clinical history was collected. We examined the influence of demographic, clinical, and audiological factors on vocabulary outcomes and early intervention efforts.<b>Results: </b>We found that this sample showed spoken language vocabulary delays (production) relative to hearing peers and showed room for improvement in rates of early diagnosis and intervention. These delays in vocabulary and early support services were predicted by an overlapping subset of hearing-, health-, and home-related variables.<b>Conclusions: </b>In a diverse sample of DHH children receiving early intervention, we identify variables that predict delays in vocabulary and early support services, which reflected <i>both </i>dimensions that are immutable, and those that clinicians and caretakers can potentially alter. We provide a discussion on the implications for clinical practice.<br><b>Supplemental Material S1.</b> Additional diagnoses (<i>n</i> = 39): number of participants in our sample diagnosed with other conditions.<br><b>Supplemental Material S2. </b>Audiological characteristics of the sample.<br><b>Supplemental Material S3.</b> Language and communication characteristics of the sample: number of participants by language background and communication method.<br><b>Supplemental Material S4.</b> Unstandardized beta weights (months of vocabulary delay) for the model of vocabulary delay selected by AIC.<br><b>Supplemental Material S5. </b>Unstandardized beta weights (months) for the model of age at diagnosis selected by AIC.<br><b>Supplemental Material S6. </b>Unstandardized beta weights (months) for the model of age at diagnosis intervention by AIC.<br>Campbell, E., & Bergelson, E. (2022). Characterizing North Carolina Deaf and Hard of Hearing infants and toddles: Predictors of vocabulary, diagnosis, and intervention. <i>Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2022_JSLHR-21-00245
DOI: 10.23641/asha.19449839.v1
title: Vocabulary, diagnosis, and intervention in DHH infants/toddlers (Campbell & Bergelson, 2022)

description: <b>Purpose: </b>This study sought to (a) characterize the demographic, audiological, and intervention variability in a population of Deaf and Hard of Hearing (DHH) children receiving state services for hearing loss; (b) identify predictors of vocabulary delays; and (c) evaluate factors influencing the success and timing of early identification and intervention efforts at a state level.<b>Method: </b>One hundred DHH infants and toddlers (aged 4–36 months) enrolled in early intervention completed the MacArthur–Bates Communicative Development Inventories, and detailed information about their audiological and clinical history was collected. We examined the influence of demographic, clinical, and audiological factors on vocabulary outcomes and early intervention efforts.<b>Results: </b>We found that this sample showed spoken language vocabulary delays (production) relative to hearing peers and showed room for improvement in rates of early diagnosis and intervention. These delays in vocabulary and early support services were predicted by an overlapping subset of hearing-, health-, and home-related variables.<b>Conclusions: </b>In a diverse sample of DHH children receiving early intervention, we identify variables that predict delays in vocabulary and early support services, which reflected <i>both </i>dimensions that are immutable, and those that clinicians and caretakers can potentially alter. We provide a discussion on the implications for clinical practice.<br><b>Supplemental Material S1.</b> Additional diagnoses (<i>n</i> = 39): number of participants in our sample diagnosed with other conditions.<br><b>Supplemental Material S2. </b>Audiological characteristics of the sample.<br><b>Supplemental Material S3.</b> Language and communication characteristics of the sample: number of participants by language background and communication method.<br><b>Supplemental Material S4.</b> Unstandardized beta weights (months of vocabulary delay) for the model of vocabulary delay selected by AIC.<br><b>Supplemental Material S5. </b>Unstandardized beta weights (months) for the model of age at diagnosis selected by AIC.<br><b>Supplemental Material S6. </b>Unstandardized beta weights (months) for the model of age at diagnosis intervention by AIC.<br>Campbell, E., & Bergelson, E. (2022). Characterizing North Carolina Deaf and Hard of Hearing infants and toddles: Predictors of vocabulary, diagnosis, and intervention. <i>Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2022_JSLHR-21-00245
DOI: 10.18739/a2vm42z6x
title: Western Greenland Ice Sheet ice temperature profiles, 2010-2012

description: Basal thermal conditions and temperature structure of the ice are important controls on motion of the Greenland ice sheet. Based on this motivation, temperatures were measured at the ice sheet bed and through the ice column in boreholes drilled to using hot water methods in western Greenland. Boreholes were drilled, and temperatures measured, in 2010, 2011, and 2012 at sites extending up to 46 kilometers (km) from the terminus of Isunnguata Sermia, a land terminating outlet glacier. Measurements are presented in 10 boreholes were drilled through the full ice column ranging from 100 meters (m) to &gt; 820 m thickness, and in an additional borehole drilled to 700 m depth in the Isunnguata Sermia outlet trough that did not intersect the ice sheet bed.
DOI: 10.5517/ccdc.csd.cc279984
title: CCDC 2064515: Experimental Crystal Structure Determination

description: Related Article: Ling-Yun Wang, Zheng-Fei Liu, Kun-Xu Teng, Li-Ya Niu, Qing-Zheng Yang|2022|Chem.Commun.|58|3807|doi:10.1039/D1CC06051A
DOI: 10.5517/ccdc.csd.cc2799c7
title: CCDC 2064518: Experimental Crystal Structure Determination

description: Related Article: Ling-Yun Wang, Zheng-Fei Liu, Kun-Xu Teng, Li-Ya Niu, Qing-Zheng Yang|2022|Chem.Commun.|58|3807|doi:10.1039/D1CC06051A
description: Related Article: lingyun Wang|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2799f9
title: CCDC 2064520: Experimental Crystal Structure Determination

description: Related Article: lingyun Wang|2021|CSD Communication|||
description: Related Article: Ling-Yun Wang, Zheng-Fei Liu, Kun-Xu Teng, Li-Ya Niu, Qing-Zheng Yang|2022|Chem.Commun.|58|3807|doi:10.1039/D1CC06051A
DOI: 10.5517/ccdc.csd.cc27vqhb
title: CCDC 2081262: Experimental Crystal Structure Determination

description: Related Article: Saeed Ataie, Seth Hogeterp, Jeffrey S. Ovens, R. Tom Baker|2022|Chem.Commun.|58|3795|doi:10.1039/D1CC06981K
DOI: 10.5517/ccdc.csd.cc27vqjc
title: CCDC 2081263: Experimental Crystal Structure Determination

description: Related Article: Saeed Ataie, Seth Hogeterp, Jeffrey S. Ovens, R. Tom Baker|2022|Chem.Commun.|58|3795|doi:10.1039/D1CC06981K
DOI: 10.5517/ccdc.csd.cc27vqkd
title: CCDC 2081264: Experimental Crystal Structure Determination

description: Related Article: Saeed Ataie, Seth Hogeterp, Jeffrey S. Ovens, R. Tom Baker|2022|Chem.Commun.|58|3795|doi:10.1039/D1CC06981K
DOI: 10.5517/ccdc.csd.cc27vqlf
title: CCDC 2081265: Experimental Crystal Structure Determination

description: Related Article: Saeed Ataie, Seth Hogeterp, Jeffrey S. Ovens, R. Tom Baker|2022|Chem.Commun.|58|3795|doi:10.1039/D1CC06981K
DOI: 10.5517/ccdc.csd.cc27vqmg
title: CCDC 2081266: Experimental Crystal Structure Determination

description: Related Article: Saeed Ataie, Seth Hogeterp, Jeffrey S. Ovens, R. Tom Baker|2022|Chem.Commun.|58|3795|doi:10.1039/D1CC06981K
DOI: 10.5517/ccdc.csd.cc27wylp
title: CCDC 2082443: Experimental Crystal Structure Determination

description: Related Article: Ya-Jie Kong, Guo-Zheng Hou, Zhao-Ning Gong, Feng-Tan Zhao, Li-Juan Han|2022|RSC Advances|12|8435|doi:10.1039/D2RA00077F
DOI: 10.5517/ccdc.csd.cc27zb5r
title: CCDC 2084724: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Pasyukov, Maxim A. Shevchenko, Konstantin E. Shepelenko, Oleg V. Khazipov, Julia V. Burykina, Evgeniy G. Gordeev, Mikhail E. Minyaev, Victor M. Chernyshev, Valentine P. Ananikov|2022|Angew.Chem.,Int.Ed.|61|e202116131|doi:10.1002/anie.202116131
DOI: 10.5517/ccdc.csd.cc27zb6s
title: CCDC 2084725: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Pasyukov, Maxim A. Shevchenko, Konstantin E. Shepelenko, Oleg V. Khazipov, Julia V. Burykina, Evgeniy G. Gordeev, Mikhail E. Minyaev, Victor M. Chernyshev, Valentine P. Ananikov|2022|Angew.Chem.,Int.Ed.|61|e202116131|doi:10.1002/anie.202116131
DOI: 10.5517/ccdc.csd.cc27zb7t
title: CCDC 2084726: Experimental Crystal Structure Determination

description: Related Article: Dmitry V. Pasyukov, Maxim A. Shevchenko, Konstantin E. Shepelenko, Oleg V. Khazipov, Julia V. Burykina, Evgeniy G. Gordeev, Mikhail E. Minyaev, Victor M. Chernyshev, Valentine P. Ananikov|2022|Angew.Chem.,Int.Ed.|61|e202116131|doi:10.1002/anie.202116131
DOI: 10.5517/ccdc.csd.cc282tmt
title: CCDC 2088086: Experimental Crystal Structure Determination

description: Related Article: Yang Jiang, Zhen Zhang, Shihui Li, Dongmei Cui|2022|Angew.Chem.,Int.Ed.|61|e202112966|doi:10.1002/anie.202112966
DOI: 10.5517/ccdc.csd.cc282try
title: CCDC 2088090: Experimental Crystal Structure Determination

description: Related Article: Yang Jiang, Zhen Zhang, Shihui Li, Dongmei Cui|2022|Angew.Chem.,Int.Ed.|61|e202112966|doi:10.1002/anie.202112966
DOI: 10.5517/ccdc.csd.cc282tsz
title: CCDC 2088091: Experimental Crystal Structure Determination

description: Related Article: Yang Jiang, Zhen Zhang, Shihui Li, Dongmei Cui|2022|Angew.Chem.,Int.Ed.|61|e202112966|doi:10.1002/anie.202112966
DOI: 10.5517/ccdc.csd.cc28dj18
title: CCDC 2097399: Experimental Crystal Structure Determination

description: Related Article: Pedro O. Abate, Analía M. Peyrot, Xavier Fontrodona, Isabel Romero, Florencia Fagalde, Néstor E. Katz|2022|RSC Advances|12|8414|doi:10.1039/D1RA09455F
DOI: 10.5517/ccdc.csd.cc27xvcd
title: CCDC 2083304: Experimental Crystal Structure Determination

description: Related Article: Parth Patel, Unnati Patel, Bhavesh Parmar, Abhishek Dadhania, Eringathodi Suresh|2022|ACS Appl. Nano Mater.|5|3712|doi:10.1021/acsanm.1c04380
DOI: 10.5517/ccdc.csd.cc296cgc
title: CCDC 2121282: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296chd
title: CCDC 2121283: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296cjf
title: CCDC 2121284: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296ckg
title: CCDC 2121285: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296clh
title: CCDC 2121286: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296cmj
title: CCDC 2121287: Experimental Crystal Structure Determination

description: Related Article: Masamichi Kishino, Satoko Takaoka, Yuki Shibutani, Shuhei Kusumoto, Kyoko Nozaki|2022|Dalton Trans.|51|5009|doi:10.1039/D2DT00513A
DOI: 10.5517/ccdc.csd.cc296p07
title: CCDC 2121578: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|716|doi:10.1021/acs.organomet.1c00643
DOI: 10.5517/ccdc.csd.cc296p18
title: CCDC 2121579: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|716|doi:10.1021/acs.organomet.1c00643
DOI: 10.5517/ccdc.csd.cc296p29
title: CCDC 2121580: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|716|doi:10.1021/acs.organomet.1c00643
DOI: 10.5517/ccdc.csd.cc296p5d
title: CCDC 2121583: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|716|doi:10.1021/acs.organomet.1c00643
DOI: 10.5517/ccdc.csd.cc296p6f
title: CCDC 2121584: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|716|doi:10.1021/acs.organomet.1c00643
DOI: 10.5517/ccdc.csd.cc298p5g
title: CCDC 2123505: Experimental Crystal Structure Determination

description: Related Article: Daniel Blasco, María Rodríguez-Castillo, M. Elena Olmos, Miguel Monge, José M. López-de-Luzuriaga|2022|RSC Advances|12|8466|doi:10.1039/D2RA00368F
DOI: 10.5517/ccdc.csd.cc29bdy0
title: CCDC 2125172: Experimental Crystal Structure Determination

description: Related Article: Parth Patel, Unnati Patel, Bhavesh Parmar, Abhishek Dadhania, Eringathodi Suresh|2022|ACS Appl. Nano Mater.|5|3712|doi:10.1021/acsanm.1c04380
DOI: 10.5517/ccdc.csd.cc29bdz1
title: CCDC 2125173: Experimental Crystal Structure Determination

description: Related Article: Parth Patel, Unnati Patel, Bhavesh Parmar, Abhishek Dadhania, Eringathodi Suresh|2022|ACS Appl. Nano Mater.|5|3712|doi:10.1021/acsanm.1c04380
DOI: 10.5517/ccdc.csd.cc29gwth
title: CCDC 2129477: Experimental Crystal Structure Determination

description: Related Article: Bin Li, Blaise L. Geoghegan, Hanns M. Weinert, Christoph Wölper, George E. Cutsail, Stephan Schulz|2022|Chem.Commun.|58|4372|doi:10.1039/D2CC00216G
DOI: 10.5517/ccdc.csd.cc29gwvj
title: CCDC 2129478: Experimental Crystal Structure Determination

description: Related Article: Bin Li, Blaise L. Geoghegan, Hanns M. Weinert, Christoph Wölper, George E. Cutsail, Stephan Schulz|2022|Chem.Commun.|58|4372|doi:10.1039/D2CC00216G
DOI: 10.5517/ccdc.csd.cc29gwwk
title: CCDC 2129479: Experimental Crystal Structure Determination

description: Related Article: Bin Li, Blaise L. Geoghegan, Hanns M. Weinert, Christoph Wölper, George E. Cutsail, Stephan Schulz|2022|Chem.Commun.|58|4372|doi:10.1039/D2CC00216G
DOI: 10.5517/ccdc.csd.cc29gwxl
title: CCDC 2129480: Experimental Crystal Structure Determination

description: Related Article: Bin Li, Blaise L. Geoghegan, Hanns M. Weinert, Christoph Wölper, George E. Cutsail, Stephan Schulz|2022|Chem.Commun.|58|4372|doi:10.1039/D2CC00216G
DOI: 10.5517/ccdc.csd.cc29gwym
title: CCDC 2129481: Experimental Crystal Structure Determination

description: Related Article: Bin Li, Blaise L. Geoghegan, Hanns M. Weinert, Christoph Wölper, George E. Cutsail, Stephan Schulz|2022|Chem.Commun.|58|4372|doi:10.1039/D2CC00216G
DOI: 10.5517/ccdc.csd.cc29w8zg
title: CCDC 2141386: Experimental Crystal Structure Determination

description: Related Article: Lu Wang, Yin Zhang, An-Qi Miao, Tian-Shu Zhang, Xiang Wang, Wen-Juan Hao, Shu-Jiang Tu, Bo Jiang|2022|Chem.Commun.|58|4376|doi:10.1039/D2CC00206J
DOI: 10.5517/ccdc.csd.cc2b9qds
title: CCDC 2154295: Experimental Crystal Structure Determination

description: Related Article: Heng-Hui Li, Shaoyu Li, Jun Kee Cheng, Shao-Hua Xiang, Bin Tan|2022|Chem.Commun.|58|4392|doi:10.1039/D2CC01212J
DOI: 10.5517/ccdc.csd.cc29m3nq
title: CCDC 2133533: Experimental Crystal Structure Determination

description: Related Article: Savannah C. Zacharias, Gaëlle Ramon, Susan A. Bourne|2022|CrystEngComm|24|2393|doi:10.1039/D2CE00039C
DOI: 10.5281/zenodo.6406948
title: Dataset: Nuclear moments of indium isotopes reveal abrupt change at magic number 82

description: Dataset for the figures in the paper 'Nuclear moments of indium isotopes reveal abrupt<br> change at magic number 82' Preprint DOI: https://doi.org/10.21203/rs.3.rs-611360/v1
DOI: 10.6084/m9.figshare.13179908
title: Optimal experimental designs for ordinal models with mixed factors for industrial and healthcare applications

description: Models with ordinal outcomes are an important part of generalized linear models and design issues for them are less studied, especially when the model has discrete and continuous factors. We propose an effective and flexible Particle Swarm Optimization (PSO) algorithm for finding locally <i>D</i>-optimal approximate designs for experiments with ordinal outcomes modeled using the cumulative logit link. We apply our technique to obtain a locally <i>D</i>-optimal approximate design for an odor removal experiment with both discrete and continuous factors and show that this design is superior to the design obtained by discretizing the continuous factor. Additionally, we find a pseudo-Bayesian <i>D</i>-optimal approximate design for this problem and study the performance of both designs under a range of plausible parameter values. We also (i) demonstrate PSO’s versatility by finding locally <i>D</i>-optimal approximate designs for a manufacturing example with surface defects and multiple continuous factors, and (ii) use PSO to find other optimal designs for estimating percentiles in a dose-response study.
DOI: 10.6084/m9.figshare.13179908.v1
title: Optimal experimental designs for ordinal models with mixed factors for industrial and healthcare applications

description: Models with ordinal outcomes are an important part of generalized linear models and design issues for them are less studied, especially when the model has discrete and continuous factors. We propose an effective and flexible Particle Swarm Optimization (PSO) algorithm for finding locally <i>D</i>-optimal approximate designs for experiments with ordinal outcomes modeled using the cumulative logit link. We apply our technique to obtain a locally <i>D</i>-optimal approximate design for an odor removal experiment with both discrete and continuous factors and show that this design is superior to the design obtained by discretizing the continuous factor. Additionally, we find a pseudo-Bayesian <i>D</i>-optimal approximate design for this problem and study the performance of both designs under a range of plausible parameter values. We also (i) demonstrate PSO’s versatility by finding locally <i>D</i>-optimal approximate designs for a manufacturing example with surface defects and multiple continuous factors, and (ii) use PSO to find other optimal designs for estimating percentiles in a dose-response study.
DOI: 10.21223/kjy3xi/uw7pvk
title: Homa_Bay_ECD_CAREGIVERS_SURVEY_DATA.tab
DOI: 10.18739/a2qv3c51q
title: Western Greenland Ice Sheet ice temperature profiles, 2010-2012

description: Basal thermal conditions and temperature structure of the ice are important controls on motion of the Greenland ice sheet. Based on this motivation, temperatures were measured at the ice sheet bed and through the ice column in boreholes drilled to using hot water methods in western Greenland. Boreholes were drilled, and temperatures measured, in 2010, 2011, and 2012 at sites extending up to 46 kilometers (km) from the terminus of Isunnguata Sermia, a land terminating outlet glacier. Measurements are presented in 10 boreholes were drilled through the full ice column ranging from 100 meters (m) to &gt; 820 m thickness, and in an additional borehole drilled to 700 m depth in the Isunnguata Sermia outlet trough that did not intersect the ice sheet bed.
DOI: 10.6073/pasta/57282aff9a884449864399012e36abf2
title: Surface pelagic total nitrogen concentrations in Lake Sunapee, USA 2009 to 2012

description: Total nitrogen concentrations were sampled from 2009 to 2012 in Lake Sunapee (Sunapee, NH, USA). The dataset consists of total nitrogen with two to three lab replicates measured at two pelagic sites in Lake Sunapee: 1) Station 210 (S210) of the New Hampshire Department of Environmental Services Volunteer Lake Assessment Program, and 2) the LSPA-GLEON buoy (Buoy) in the main basin of the lake. The samples were collected at the surface, five to six times per year, ranging from June to September each year. The mean total nitrogen concentration with its standard deviation from lab replicates is presented.