cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5206427
title: TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021

description: This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.
DOI: 10.15468/42p5qw
title: Miscellaneous Families of Guam Coleoptera

description: This dataset contains the digitized treatments in Plazi based on the original book chapter Swezey, O. H. (1942): Miscellaneous Families of Guam Coleoptera. In: Insects of Guam I. Honolulu, Hawaii: Bernice P. Bishop Museum: 150-171, DOI: http://doi.org/10.5281/zenodo.5167701
DOI: 10.5281/zenodo.5206458
title: Dataset for RECOMS ESR01

description: Dataset for RECOMS ESR01
DOI: 10.5281/zenodo.5206457
title: Dataset for RECOMS ESR01

description: Dataset for RECOMS ESR01
DOI: 10.5281/zenodo.5206476
title: TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021

description: This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.
DOI: 10.5281/zenodo.5123509
title: TCTracer: Establishing Test-to-Code Traceability Links Using Dynamic and Static Techniques - Evaluation Data - Empirical Software Engineering 2021

description: This repository provides the data artefacts for the experiments conducted using our tool TCTracer for the journal paper "TCTracer: Establishing Test-to-Code Traceability links Using Dynamic and Static Techniques" as submitted to the Empirical Software Engineering journal in 2021.
DOI: 10.6084/m9.figshare.12408164
title: Cruise CTD & Current.mat

description: Data for manuscript "An unusual cross-shelf current driven by the subtle changes of wind direction in a marginal sea"
DOI: 10.6084/m9.figshare.12408164.v4
title: Cruise CTD & Current.mat

description: Data for manuscript "An unusual cross-shelf current driven by the subtle changes of wind direction in a marginal sea"
DOI: 10.6084/m9.figshare.12408164.v3
title: Cruise CTD & Current.mat

description: Data for manuscript "An unusual cross-shelf current driven by the subtle changes of wind direction in a marginal sea"
DOI: 10.6084/m9.figshare.15173400
title: Masstige luxury consumption and belonging.xlsx

description: Correlational findings on belonging and masstige luxury consumption.
DOI: 10.6084/m9.figshare.12656165.v23
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.5281/zenodo.5205553
title: STonKGs Location Model

description: The fine-tuned model trained on subcellular location annotations.
DOI: 10.5281/zenodo.5205552
title: STonKGs Location Model

description: The fine-tuned model trained on subcellular location annotations.
DOI: 10.5281/zenodo.5206139
title: STonKGs Correct Multiclass Model

description: The fine-tuned model trained on the multiclass formulation of correctness curations.
DOI: 10.5281/zenodo.5206138
title: STonKGs Correct Multiclass Model

description: The fine-tuned model trained on the multiclass formulation of correctness curations.
DOI: 10.5281/zenodo.5206278
title: ProbFire: a probabilistic fire early warning system for Indonesia

description: This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.
DOI: 10.5281/zenodo.5206277
title: ProbFire: a probabilistic fire early warning system for Indonesia

description: This repository holds ProbFire model input datasets and pre-trained model weights and feature scaling parameters. Model code and description is available at https://github.com/ToFEWSI/ProbFire.
DOI: 10.25375/uct.15173109.v1
title: Population density estimates of spotted hyaena (Crocuta crocuta) in two KwaZulu-Natal protected areas

description: Despite increasing recognition of the important ecological role large carnivores fulfil and their ability to generate income for protected areas, they remain amongst the most threatened species on Earth. Most large carnivore species have exhibited substantial population declines and geographic range contractions during the past two centuries. Key to reversing this trend is devising cost-effective monitoring methods that produce reliable estimates of abundance or density over timeframes that allow for the success or failure of conservation interventions to be measured. As both scavengers and apex predators, spotted hyaenas (<i>Crocuta crocuta</i>) play extremely important ecological roles, and it has been suggested that they are keystone predators and key indicators of ecosystem health. Although the IUCN Red List of Threatened Species lists spotted hyaenas as “Least Concern”, the overall population trend is decreasing and regional declines have been observed in some areas, such as the northern KwaZulu-Natal province of South Africa. Habitat loss and direct persecution are causing spotted hyaenas to become increasingly reliant on protected areas. In my study, I analysed hyaena by-catch data from camera trap surveys that were conducted in 2019 to monitor leopards (Panthera pardus) in two protected areas in northern KwaZulu-Natal, Mun-Ya-Wana Conservancy and the uMkhuze section of iSimangaliso Wetland Park. I used spatially explicit capture-recapture (SECR) models to estimate the population density of spotted hyaenas in both protected areas. The density of spotted hyaenas in Mun-Ya-Wana Conservancy was estimated to be 5.86 ± 1.12 individuals per 100 km<sup>2</sup>, based on 30 identified individuals in a sample area of 3122 km<sup>2</sup>. The density of spotted hyaenas in the uMkhuze section of iSimangaliso Wetland Park was estimated to be 2.97 ± 0.79 individuals per 100 km<sup>2</sup>, based on 26 identified individuals in a sample area of 2828 km<sup>2</sup>. These results confirm both the importance of new protected areas (Mun-Ya-Wana Conservancy) in reversing population declines while simultaneously showing that long established protected areas (uMkhuze section of iSimangaliso Wetland Park) may be failing to protect spotted hyaena and presumably other large carnivores. Understanding the drivers of these differences between protected areas is essential to provide regionally stable spotted hyaena populations. Routine camera trap surveys combined with SECR models provide a cost-effective way to monitor spotted hyaena populations and produce reliable estimates of population density. Once more accurate, long-term data on the size and trends of spotted hyaena subpopulations both within and outside protected areas have been collected, the status of spotted hyaenas should be reassessed.<br>
DOI: 10.25375/uct.15173109
title: Population density estimates of spotted hyaena (Crocuta crocuta) in two KwaZulu-Natal protected areas

description: Despite increasing recognition of the important ecological role large carnivores fulfil and their ability to generate income for protected areas, they remain amongst the most threatened species on Earth. Most large carnivore species have exhibited substantial population declines and geographic range contractions during the past two centuries. Key to reversing this trend is devising cost-effective monitoring methods that produce reliable estimates of abundance or density over timeframes that allow for the success or failure of conservation interventions to be measured. As both scavengers and apex predators, spotted hyaenas (<i>Crocuta crocuta</i>) play extremely important ecological roles, and it has been suggested that they are keystone predators and key indicators of ecosystem health. Although the IUCN Red List of Threatened Species lists spotted hyaenas as “Least Concern”, the overall population trend is decreasing and regional declines have been observed in some areas, such as the northern KwaZulu-Natal province of South Africa. Habitat loss and direct persecution are causing spotted hyaenas to become increasingly reliant on protected areas. In my study, I analysed hyaena by-catch data from camera trap surveys that were conducted in 2019 to monitor leopards (Panthera pardus) in two protected areas in northern KwaZulu-Natal, Mun-Ya-Wana Conservancy and the uMkhuze section of iSimangaliso Wetland Park. I used spatially explicit capture-recapture (SECR) models to estimate the population density of spotted hyaenas in both protected areas. The density of spotted hyaenas in Mun-Ya-Wana Conservancy was estimated to be 5.86 ± 1.12 individuals per 100 km<sup>2</sup>, based on 30 identified individuals in a sample area of 3122 km<sup>2</sup>. The density of spotted hyaenas in the uMkhuze section of iSimangaliso Wetland Park was estimated to be 2.97 ± 0.79 individuals per 100 km<sup>2</sup>, based on 26 identified individuals in a sample area of 2828 km<sup>2</sup>. These results confirm both the importance of new protected areas (Mun-Ya-Wana Conservancy) in reversing population declines while simultaneously showing that long established protected areas (uMkhuze section of iSimangaliso Wetland Park) may be failing to protect spotted hyaena and presumably other large carnivores. Understanding the drivers of these differences between protected areas is essential to provide regionally stable spotted hyaena populations. Routine camera trap surveys combined with SECR models provide a cost-effective way to monitor spotted hyaena populations and produce reliable estimates of population density. Once more accurate, long-term data on the size and trends of spotted hyaena subpopulations both within and outside protected areas have been collected, the status of spotted hyaenas should be reassessed.<br>
DOI: 10.6084/m9.figshare.15173181.v1
title: Girardinichthys_multiradiatus.201028.tar.gz

description: Genome assembly and annotation of the Darkedged Splitfin, <i>Girardinichthys multiradiatus.</i>
DOI: 10.15468/jzb8fu
title: Hymenoptera Formicidae of Guam

description: This dataset contains the digitized treatments in Plazi based on the original book chapter Swezey, O. H. (1942): Hymenoptera Formicidae of Guam. In: Insects of Guam I. Honolulu, Hawaii: Bernice P. Bishop Museum: 175-183, DOI: http://doi.org/10.5281/zenodo.5160270
DOI: 10.6084/m9.figshare.13516502
title: Table S2.xlsx

description: Mean relative abundance of core microbes responded to orlistat
DOI: 10.6084/m9.figshare.13516502.v3
title: Table S2.xlsx

description: Mean relative abundance of core microbes responded to orlistat
DOI: 10.6084/m9.figshare.13516502.v2
title: Table S2.xlsx

description: Mean relative abundance of core microbes responded to orlistat
DOI: 10.6084/m9.figshare.13516508
title: Table S1.xlsx

description: Demographic characteristics of patients in orlistat and ezetimibe intervention group
DOI: 10.5281/zenodo.5206708
title: Field dataset for marsh intertidal creek study - Sapelo Island, Georgia

description: Field data collected in an intertidal salt marsh located on Sapelo Island, USA. This data contains processed datasets for acoustic Doppler velocimeters (ADVs), pressure sensors, and TSS water column samples within three creeks in a confined drainage basin.
DOI: 10.5281/zenodo.5206707
title: Field dataset for marsh intertidal creek study - Sapelo Island, Georgia

description: Field data collected in an intertidal salt marsh located on Sapelo Island, USA. This data contains processed datasets for acoustic Doppler velocimeters (ADVs), pressure sensors, and TSS water column samples within three creeks in a confined drainage basin.
DOI: 10.6084/m9.figshare.13516514
title: Table S3.xlsx

description: Mean relative abundance of core microbes responded to ezetimibe
DOI: 10.6084/m9.figshare.13516514.v4
title: Table S3.xlsx

description: Mean relative abundance of core microbes responded to ezetimibe
DOI: 10.6084/m9.figshare.13516514.v3
title: Table S3.xlsx

description: Mean relative abundance of core microbes responded to ezetimibe
DOI: 10.6084/m9.figshare.15173451
title: Cookies Rich in Iron (Fe), Folic Acid and Cobalamin : A Novel Functional Food for Adolescent with Anemia

description: The data uploaded in this repository is RAW Data for: Cookies Rich in Iron (Fe), Folic Acid and Cobalamin : A Novel Functional Food for Adolescent with Anemia
DOI: 10.6084/m9.figshare.15173181
title: Girardinichthys_multiradiatus.201028.tar.gz

description: Genome assembly and annotation of the Darkedged Splitfin, <i>Girardinichthys multiradiatus.</i>
DOI: 10.6084/m9.figshare.15173181.v2
title: Girardinichthys_multiradiatus.201028.tar.gz

description: Genome assembly and annotation of the Darkedged Splitfin, <i>Girardinichthys multiradiatus.</i>
DOI: 10.21223/hwmpbr
title: Dataset for: Genetic mapping of late blight resistance originating from wild Solanum germplasm

description: Genotypes from biparental population of S. cajamarquense and self-compatible 2x hybrid derived from crossing 2x landraces with SC sources of S. chacoense were exposed to late blight under natural infection in Chontabamba, Oxapampa (1810 masl Pasco-Perú) during two rainy seasons. Statistical randomized complete block design (RBDC) with three replications of 6 and 8 hill plots each season were properly established. After 40 days after planting (DAP), the percentage of leaf area affected by late blight infection was recorded by plot throughout the season to subsequently compute the area under the disease progress curve (AUDPC). AUDPC values are used to compute the predicted susceptibility scale value of any candidate genotype within a scale where increasing numbers represent increasing susceptibility.
DOI: 10.5281/zenodo.5206596
title: MigrationsKB: A Knowledge Base of Migration related annotated Tweets

description: <strong>MigrationsKB(MGKB)</strong> is a public Knowledge Base of anonymized <strong>Migration</strong> related <strong>annotated</strong> tweets. The MGKB currently contains over <strong>200 thousand</strong> tweets, spanning over 9 years (January 2013 to July 2021), filtered with 11 European countries of <em>the United Kingdom, Germany, Spain, Poland, France, Sweden, Austria, Hungary, Switzerland, Netherlands and Italy</em>. <strong>Metadata</strong> information about the tweets, such as Geo information (<strong>place name</strong>, <strong>coordinates</strong>, <strong>country code</strong>). <strong>MGKB</strong> contains <strong>entities</strong>, <strong>sentiments</strong>, <strong>hate speeches</strong>, <strong>topics</strong>, <strong>hashtags</strong>, <em>encrypted user mentions</em> in RDF format. The schema of <strong>MGKB</strong> is an extension of TweetsKB for migrations related information. Moreover, to associate and represent the potential economic and social factors driving the migration flows such as <strong>eurostat</strong>, <strong>statista</strong>, etc. FIBO ontology was used. The extracted <strong>economic indicators</strong>, such as GDP Growth Rate, are connected with each Tweet in RDF using geographical and temporal dimensions. The user IDs and the tweet texts are encrypted for privacy purposes, while the tweet IDs are preserved. For this version, the <strong>MGKB</strong> is delivered as a whole and separately by year. The extracted entities and topic words are also published. Online SPARQL endpoint https://mgkb.fiz-karlsruhe.de/sparql/ More information please refer to the website https://migrationskb.github.io/MGKB/. Please contact Yiyi Chen (yiyi.chen@partner.kit.edu) for pretrained models (sentiment analysis/hate speech detection/ETM model) if necessary.
DOI: 10.6084/m9.figshare.15173484.v1
title: Project: Effect of mutation type on expression levels

description: Expression data with sample annotations about mutation and expression level (FPKM) for select genes.
DOI: 10.5281/zenodo.5203043
title: Supplementary Material for Hasnat's Thesis 1/n

description: Supplementary Material for Hasnat's Thesis 1/n
DOI: 10.5281/zenodo.5205418
title: MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors

description: <strong>MigrationsKB(MGKB)</strong> is a public Knowledge Base of anonymized <strong>Migration</strong> related <strong>annotated</strong> tweets. The MGKB currently contains over <strong>200 thousand</strong> tweets, spanning over 9 years (January 2013 to July 2021), filtered with 11 European countries of <em>the United Kingdom, Germany, Spain, Poland, France, Sweden, Austria, Hungary, Switzerland, Netherlands and Italy</em>. <strong>Metadata</strong> information about the tweets, such as Geo information (<strong>place name</strong>, <strong>coordinates</strong>, <strong>country code</strong>). <strong>MGKB</strong> contains <strong>entities</strong>, <strong>sentiments</strong>, <strong>hate speeches</strong>, <strong>topics</strong>, <strong>hashtags</strong>, <em>encrypted user mentions</em> in RDF format. The schema of <strong>MGKB</strong> is an extension of TweetsKB for migrations related information. Moreover, to associate and represent the potential economic and social factors driving the migration flows such as <strong>eurostat</strong>, <strong>statista</strong>, etc. FIBO ontology was used. The extracted <strong>economic indicators</strong>, such as GDP Growth Rate, are connected with each Tweet in RDF using geographical and temporal dimensions. The user IDs and the tweet texts are encrypted for privacy purposes, while the tweet IDs are preserved. For this version, the <strong>MGKB</strong> is delivered as a whole and separately by year. The trained BERT model (sentiment analysis), CNN-Bilstm-Attention model (hate speech detection) and ETM model (topic modeling) are also provided. In addition, the extracted entities using Blink entity linking are provided.
DOI: 10.5517/ccphhh4
title: CCDC 640506: Experimental Crystal Structure Determination

description: Related Article: J.Zinczuk, E.A.Ruveda, H.W.Thompson, R.A.Lalancette|2007|Acta Crystallogr.,Sect.E:Struct.Rep.Online|63|o1490|doi:10.1107/S1600536807007982
DOI: 10.5517/ccdc.csd.cc249lgj
title: CCDC 1975427: Experimental Crystal Structure Determination

description: Related Article: H. A. Nkabyo, O. R. Oyenihi, O. Olaoye, P. Sikiti, G. W. Bosman, R. C. Luckay|2021|New J.Chem.|45|14703|doi:10.1039/D1NJ02320A
DOI: 10.5517/ccdc.csd.cc25r0ts
title: CCDC 2018125: Experimental Crystal Structure Determination

description: Related Article: Feng-Zhen Hua, Chao Feng, Ling-Mei Zhang, Yu-Meng Yang, Guo-Ning Zhang, Yu-Cheng Wang, Hong Zhao|2021|CrystEngComm|23|5463|doi:10.1039/D1CE00621E
DOI: 10.5517/ccdc.csd.cc25r0wv
title: CCDC 2018127: Experimental Crystal Structure Determination

description: Related Article: Feng-Zhen Hua, Chao Feng, Ling-Mei Zhang, Yu-Meng Yang, Guo-Ning Zhang, Yu-Cheng Wang, Hong Zhao|2021|CrystEngComm|23|5463|doi:10.1039/D1CE00621E
DOI: 10.5517/ccdc.csd.cc261jng
title: CCDC 2027265: Experimental Crystal Structure Determination

description: Related Article: Zhu-Meng Ren, Lu-Lu Wang, Jin-Miao Wang, Bin Zhu, Qiang Gao, Mei Wang, Feng Shao, Yu-Hua Fan|2021|CrystEngComm|23|5475|doi:10.1039/D1CE00762A
DOI: 10.5517/ccdc.csd.cc2620q1
title: CCDC 2027732: Experimental Crystal Structure Determination

description: Related Article: Zhu-Meng Ren, Lu-Lu Wang, Jin-Miao Wang, Bin Zhu, Qiang Gao, Mei Wang, Feng Shao, Yu-Hua Fan|2021|CrystEngComm|23|5475|doi:10.1039/D1CE00762A
DOI: 10.5517/ccdc.csd.cc2620s3
title: CCDC 2027734: Experimental Crystal Structure Determination

description: Related Article: Zhu-Meng Ren, Lu-Lu Wang, Jin-Miao Wang, Bin Zhu, Qiang Gao, Mei Wang, Feng Shao, Yu-Hua Fan|2021|CrystEngComm|23|5475|doi:10.1039/D1CE00762A
DOI: 10.5517/ccdc.csd.cc265dng
title: CCDC 2030985: Experimental Crystal Structure Determination

description: Related Article: Peipei Cen, Zixin He, Runmei Ding, Huifang Yang, Li Li, Yi-Quan Zhang, Yonghong Li, Danian Tian, Xiangyu Liu|2021|CrystEngComm|23|5443|doi:10.1039/D1CE00682G
DOI: 10.5517/ccdc.csd.cc26833q
title: CCDC 2033572: Experimental Crystal Structure Determination

description: Related Article: Peipei Cen, Zixin He, Runmei Ding, Huifang Yang, Li Li, Yi-Quan Zhang, Yonghong Li, Danian Tian, Xiangyu Liu|2021|CrystEngComm|23|5443|doi:10.1039/D1CE00682G
DOI: 10.5517/ccdc.csd.cc26hg4b
title: CCDC 2040641: Experimental Crystal Structure Determination

description: Related Article: Yoshino Fujii, Yuta Taguchi, Shugo Tokai, Yasuhiro Matsumoto, Naruhiro Yoshida, Tetsuo Iwasawa|2021|Tetrahedron|95|132353|doi:10.1016/j.tet.2021.132353
DOI: 10.5517/ccdc.csd.cc26hgmt
title: CCDC 2040656: Experimental Crystal Structure Determination

description: Related Article: Yoshino Fujii, Yuta Taguchi, Shugo Tokai, Yasuhiro Matsumoto, Naruhiro Yoshida, Tetsuo Iwasawa|2021|Tetrahedron|95|132353|doi:10.1016/j.tet.2021.132353
DOI: 10.5517/ccdc.csd.cc26jmr4
title: CCDC 2041776: Experimental Crystal Structure Determination

description: Related Article: Luzhen Jiao, Guorui Cao, Dawei Teng|2021|New J.Chem.|45|14401|doi:10.1039/D1NJ01284C
DOI: 10.5517/ccdc.csd.cc26rnsf
title: CCDC 2048535: Experimental Crystal Structure Determination

description: Related Article: Feng-Zhen Hua, Chao Feng, Ling-Mei Zhang, Yu-Meng Yang, Guo-Ning Zhang, Yu-Cheng Wang, Hong Zhao|2021|CrystEngComm|23|5463|doi:10.1039/D1CE00621E
DOI: 10.5517/ccdc.csd.cc270t8c
title: CCDC 2056362: Experimental Crystal Structure Determination

description: Related Article: Zhu-Meng Ren, Lu-Lu Wang, Jin-Miao Wang, Bin Zhu, Qiang Gao, Mei Wang, Feng Shao, Yu-Hua Fan|2021|CrystEngComm|23|5475|doi:10.1039/D1CE00762A
DOI: 10.5517/ccdc.csd.cc270t9d
title: CCDC 2056363: Experimental Crystal Structure Determination

description: Related Article: Zhu-Meng Ren, Lu-Lu Wang, Jin-Miao Wang, Bin Zhu, Qiang Gao, Mei Wang, Feng Shao, Yu-Hua Fan|2021|CrystEngComm|23|5475|doi:10.1039/D1CE00762A
DOI: 10.5517/ccdc.csd.cc214vdg
title: CCDC 1881495: Experimental Crystal Structure Determination

description: Related Article: Jian-Ling Ni, Yu Liang, Juan-Juan Shao, Jun-Feng Li, Guang-Jun Li, Li-Zhuang Chen, Fang-Ming Wang|2021|CrystEngComm|23|5516|doi:10.1039/D1CE00499A
DOI: 10.5517/ccdc.csd.cc214vbd
title: CCDC 1881493: Experimental Crystal Structure Determination

description: Related Article: Jian-Ling Ni, Yu Liang, Juan-Juan Shao, Jun-Feng Li, Guang-Jun Li, Li-Zhuang Chen, Fang-Ming Wang|2021|CrystEngComm|23|5516|doi:10.1039/D1CE00499A
DOI: 10.5517/ccdc.csd.cc275d83
title: CCDC 2060764: Experimental Crystal Structure Determination

description: Related Article: Helge-Boj Hansen, Hubert Wadepohl, Markus Enders|2021|Chem.-Eur.J.|27|11084|doi:10.1002/chem.202101586
DOI: 10.5517/ccdc.csd.cc275d94
title: CCDC 2060765: Experimental Crystal Structure Determination

description: Related Article: Helge-Boj Hansen, Hubert Wadepohl, Markus Enders|2021|Chem.-Eur.J.|27|11084|doi:10.1002/chem.202101586
DOI: 10.5285/c9ae04d6-32d2-73f1-e053-6c86abc0c833
title: Wave spectra and statistics measured in 1D and 2D in the North Atlantic, October-November 2013.

description: The data included in this publication are 1D and 2D (directional) wave spectra and statistics from the HiWINGS (High Wind Air-sea Gas Exchange Study) cruise from October 9, 2013 to November 14, 2013. Data were collected using a Datawell DWR-G4 directional Waverider Buoy on the R/V Knorr KN213-3 cruise in the North Atlantic.