cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3518775
title: Raw multibeam bathymetry data collected around the Mertz glacier, East Antarcica on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).

description: <strong>Dataset abstract</strong> An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Mertz glacier, East Antarcica in the austral summer of 2016/2017. Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys. This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution. <strong>Dataset contents</strong> lineYYYYDDmonHHMMSS.xse, data file, proprietary format location.hydrostar, ancillary file, ASCII data_file_header.txt, metadata, text README.txt, metadata, text <strong>Dataset license</strong> This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals.
description: {"references": ["David W H Walton, & Jenny Thomas. (2018, November 22). Cruise Report - Antarctic Circumnavigation Expedition (ACE) 20th December 2016 - 19th March 2017 (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.1443511", "Henry, T., Robinson, C., Haumann, F. A., Thomas, J., Hutchings, J., Schuback, N., Tsukernik, M. and Leonard, K. (2019). Physical and biogeochemical oceanography data from Conductivity, Temperature, Depth (CTD) rosette deployments during the Antarctic Circumnavigation Expedition (ACE). (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247384", "Caress and Chayes, 1993. MB-System Software."]}
DOI: 10.17632/mdr8pt4rx9.1
title: COVID-19 AND E-WALLET USAGE INTENTION: A MULTIGRUP ANALYSIS BETWEEN INDONESIA AND MALAYSIA

description: The present study examines the effect of perceived risk, government support, and perceived usefulness on customers intention to use e-wallet during COVID-19 outbreak. To give more fruitful insight, another major contribution of this study is investigating the group difference between Indonesia and Malaysia in the overall model. Questionnaires are distributed to the respondents by using a proportional sampling technique. As a result, 259 total respondents from Indonesia and 207 from Malaysia are collected. Both countries are selected because Indonesia and Malaysia can be considered as the two-worst countries in ASEAN affected by COVID-19. The model is tested using PLS-Structural Equation Modeling (SEM) approach. The results show that the effects of government support on the intention to use e-wallets differ between countries. Besides, perceived usefulness fully mediated government support-intention to use e-wallets relationship, and partially mediated the effect of perceived risk on intention to use e-wallets.
DOI: 10.17632/gvp7vshvnh.1
title: Cassava root cross-section images

description: This dataset contains images of cassava root cross-sections captured by the Makerere University Artificial Intelligence Lab in conjunction with the National Crop Resources Research Institute in Uganda and the Tanzania Agricultural Research Institute in Tanzania. The images were captured during harvests conducted by cassava breeders to assess and score root necrosis for different varieties of cassava. The dataset contains 9843 images from five field trials. The images can be used to develop deep learning algorithms that can help to automate the scoring of root necrosis and to study other aspects of necrosis expression on cassava roots..
DOI: 10.17632/wg9pkrp3rv.1
title: Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines

description: A low-resolution WRF model has run which has exact same spatial resolution with GFS of NCEP which is initial and boundary conditions data and extracted data from the four grid points of this model which surround the related wind farm are used as predictor variables to the gradient boosting machines. Thus; the downscaling process is basically done by the coupling of the low-resolution numerical weather prediction model and machine learning algorithm. The results of this hybrid model are also compared with the results of a WRF model which has a higher spatial resolution in terms of both statistical measures and required time. As a result of this; the study claims a novel approach for not only increasing the accuracy of the wind power generation forecasts but also reducing the computational expense. Since wind energy increases its share in installed energy capacity thanks to its maturity in terms of technology and decreasing costs; wind power generation forecasts with high performance becomes very crucial to have not only the security of the electricity supply of the countries all around the world but also prevent energy imbalance penalties for the wind farm owners. Besides, this study shows that a better performance than the downscaling processes of the numerical weather prediction models do can be achieved in shorter periods with machine learning algorithms. Thus; this study also highlights that the coupling of machine learning algorithms with the numerical weather prediction models can play an effective role in the future in terms of atmospheric sciences. Consequently; this work provides basically not only increasing the accuracy of the wind power generation forecasts when it is compared with the numerical weather prediction models but also decreasing the computational expense by doing the downscaling process of numerical weather prediction models with gradient boosting machines.
DOI: 10.17632/wg9pkrp3rv.2
title: Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines

description: A low-resolution WRF model has run which has exact same spatial resolution with GFS of NCEP which is initial and boundary conditions data and extracted data from the four grid points of this model which surround the related wind farm are used as predictor variables to the gradient boosting machines. Thus; the downscaling process is basically done by the coupling of the low-resolution numerical weather prediction model and machine learning algorithm. The results of this hybrid model are also compared with the results of a WRF model which has a higher spatial resolution in terms of both statistical measures and required time. As a result of this; the study claims a novel approach for not only increasing the accuracy of the wind power generation forecasts but also reducing the computational expense. Since wind energy increases its share in installed energy capacity thanks to its maturity in terms of technology and decreasing costs; wind power generation forecasts with high performance becomes very crucial to have not only the security of the electricity supply of the countries all around the world but also prevent energy imbalance penalties for the wind farm owners. Besides, this study shows that a better performance than the downscaling processes of the numerical weather prediction models do can be achieved in shorter periods with machine learning algorithms. Thus; this study also highlights that the coupling of machine learning algorithms with the numerical weather prediction models can play an effective role in the future in terms of atmospheric sciences. Consequently; this work provides basically not only increasing the accuracy of the wind power generation forecasts when it is compared with the numerical weather prediction models but also decreasing the computational expense by doing the downscaling process of numerical weather prediction models with gradient boosting machines.
DOI: 10.5281/zenodo.3901350
title: Magnetic well and Mercier stability of stellarators near the magnetic axis

description: Data files and plotting scripts for figures in the paper "Magnetic well and Mercier stability of stellarators near the magnetic axis"
DOI: 10.17632/dfksjy56y3.1
title: Normative Value Of Static And Dynamic Balance Tests In Healthy Adults With Flatfoot : A Pilot Observational Study

description: BACKGROUND: Balance is considered as the process which maintains the center of gravity in between the body’s support base with joint positioning and muscular activity. Postural balance defines statically as the ability to maintain a base of support with minimal movement and dynamically as the ability to perform a task while maintaining a stable position. Deficits of the foot arch’ congruity, flexibility and strength disturb the function of foot and predispose individuals to standing balance disturbances. METHOD: Here 15 participants were taken of age group 18-30 years. Observational study was performed on the individuals with FF by performing static and dynamic balance tests i.e. Star excursion balance test, Narrow corridor walk test, One leg stance test, Functional reach test, Balance error scoring system. RESULT: In this study the result was significant in the balance scales either static or dynamic. Normative reference values were find out in the females of age group of 18-30 years with the FF. CONCLUSION: This study results gave the normative reference value of the static and dynamic balance test for the healthy adult female with FF in age group of 18-30 years.
DOI: 10.17632/p3bwbphj6x.1
title: Data for: Long-term dynamic simulation of environmental impacts on ecosystem-based pond aquaculture

description: "Fishpond simulation" folder contains an executable process simulator with the necessary auxiliary files and with a detailed guide, for demonstration purposes. "Fishpond model structure with functionalities" folder contains the model structure with the local programs of the fishpond model, as well as the description of model parameters, as follows: -FP_NewCarp_4.txt is the model, implemented in the method of Programmable Process Structures. It contains information about both the model structure and the calculating local declarative programs. It can be studied with common graph visualization tools, using .graphml format (e.g. yED graph editor, etc.). -Param_input_output variables.xlsx contains detailed information about the global variables, inputs, parameters and outputs in the application specific (locally executable) program code of the model. Referred parameters, input and output variables are explained together with listing of their initial and parameter values, as well as their literature references. "Example model outputs" folder contains some illustrative output of the developed dynamic simulation model, as follows: -Output_2010-2016.xlsx: Outputs of simulations for years 2010-2016, utilizing factual data from SzegedFish Ltd. It is discussed in Section 5.2. -Output_design_space.xlsx: Calculations with various feeding and stocking densities for a fishpond, characterizing local conditions. Necessary conditions can be found in Sheet INFO. It is discussed in Section 5.3. -Output_daily_recording.xlsx: Illustration of simulation results for NorESM RCP 4.5 and 8.5 climate scenarios, with daily output recording, for six scenarios, defined in Sheet INFO. It is discussed in Section 5.4. -Output_Oct31_recording.xlsx: Simulation results with output recording at the last day of production season, Oct 31. Contains full simulation results for NorESM RCP 4.5 and 8.5 climate scenarios for two locations in Hungary (Szeged and Debrecen). It is discussed in Section 5.4.
DOI: 10.17632/8y6s2ph9by.1
title: PhD Thesis Supplementary Material
DOI: 10.17632/wg9pkrp3rv.3
title: Wind Power Generation Forecast by Coupling Numerical Weather Prediction Model and Gradient Boosting Machines

description: A low-resolution WRF model has run which has exact same spatial resolution with GFS of NCEP which is initial and boundary conditions data and extracted data from the four grid points of this model which surround the related wind farm are used as predictor variables to the gradient boosting machines. Thus; the downscaling process is basically done by the coupling of the low-resolution numerical weather prediction model and machine learning algorithm. The results of this hybrid model are also compared with the results of a WRF model which has a higher spatial resolution in terms of both statistical measures and required time. As a result of this; the study claims a novel approach for not only increasing the accuracy of the wind power generation forecasts but also reducing the computational expense. Since wind energy increases its share in installed energy capacity thanks to its maturity in terms of technology and decreasing costs; wind power generation forecasts with high performance becomes very crucial to have not only the security of the electricity supply of the countries all around the world but also prevent energy imbalance penalties for the wind farm owners. Besides, this study shows that a better performance than the downscaling processes of the numerical weather prediction models do can be achieved in shorter periods with machine learning algorithms. Thus; this study also highlights that the coupling of machine learning algorithms with the numerical weather prediction models can play an effective role in the future in terms of atmospheric sciences. Consequently; this work provides basically not only increasing the accuracy of the wind power generation forecasts when it is compared with the numerical weather prediction models but also decreasing the computational expense by doing the downscaling process of numerical weather prediction models with gradient boosting machines.
DOI: 10.25386/genetics.11950626.v1
title: Supplemental Material for Temin, 2020

description: Supplemental Table to <br>"Analysis of a Strong Suppressor of Segregation Distortion in Drosophila melanogaster"<br>
DOI: 10.25850/nioz/7b.b.s/dbwmnx
title: 64PE398 - HR-ICP MS blank and dilution corrected trace metal data.tab
DOI: 10.25850/nioz/7b.b.s/engthk
title: 64PE398 - HR-ICP MS untreated trace metal data - drift, detection limit and blank data.tab
DOI: 10.25850/nioz/7b.b.s/8l84mg
title: 64PE398 - SPM sampling.tab
DOI: 10.25850/nioz/7b.b.s/kitecj
title: 64PE398 - Trace metal enrichment factors.tab
DOI: 10.25850/nioz/7b.b.s/p2e6xx
title: 64PE398_ST13C01.tab
DOI: 10.25850/nioz/7b.b.s/odz6id
title: 64PE398_ST16C09.tab
DOI: 10.25850/nioz/7b.b.s/7xsgns
title: 64PE398_ST26C01.tab
DOI: 10.25850/nioz/7b.b.s/odw4si
title: 64PE398_ST27C01.tab
DOI: 10.25850/nioz/7b.b.s/2qp3m6
title: 64PE398_ST27C26.tab
DOI: 10.25850/nioz/7b.b.s/0xskmy
title: 64PE398_ST27_allcasts.tab
DOI: 10.25850/nioz/7b.b.s/y4jzpn
title: 64PE398_ST28C01.tab
DOI: 10.25850/nioz/7b.b.s/edxfkb
title: 64PE398_ST32C01.tab
DOI: 10.25850/nioz/7b.b.s/lmfryo
title: 64PE398_ST37C01.tab
DOI: 10.25850/nioz/7b.b.s/xr0vmt
title: 64PE398_ST38C01.tab
DOI: 10.25850/nioz/7b.b.s/73igki
title: 64PE398_ST39C01.tab
DOI: 10.25850/nioz/7b.b.s/vbruyg
title: 64PE398_ST40C01.tab
DOI: 10.25850/nioz/7b.b.s/kqpe2m
title: 64PE398_ST42C21.tab
DOI: 10.25850/nioz/7b.b.s/bwqth4
title: 64PE398_ST44C01.tab
DOI: 10.25850/nioz/7b.b.s/nz6s6l
title: 64PE398_ST45C01.tab
DOI: 10.25850/nioz/7b.b.s/l03ikb
title: 64PE398_ST46C01.tab
DOI: 10.25850/nioz/7b.b.s/lwdb8n
title: 64PE398_ST47C01.tab
DOI: 10.25850/nioz/7b.b.s/scfo0z
title: 64PE398_ST49C01.tab
DOI: 10.25850/nioz/7b.b.s/rvrmo5
title: 64PE398_ST49C08.tab
DOI: 10.25850/nioz/7b.b.s/2ptqh1
title: 64PE398 - HR-ICP MS blank and dilution corrected trace metal data.xlsx
DOI: 10.25850/nioz/7b.b.s/a06wxv
title: 64PE398 - HR-ICP MS untreated trace metal data - drift, detection limit and blank data.xlsx
DOI: 10.25850/nioz/7b.b.s/cighck
title: 64PE398 - SPM sampling.xlsx
DOI: 10.25850/nioz/7b.b.s/58dkks
title: 64PE398 - Trace metal enrichment factors.xlsx
DOI: 10.25850/nioz/7b.b.s/8kvvw1
title: 64PE398_ST13C01.xlsx
DOI: 10.25850/nioz/7b.b.s/ts96i9
title: 64PE398_ST16C09.xlsx
DOI: 10.25850/nioz/7b.b.s/unk6oj
title: 64PE398_ST26C01.xlsx
DOI: 10.25850/nioz/7b.b.s/a8gcch
title: 64PE398_ST27C01.xlsx
DOI: 10.25850/nioz/7b.b.s/s8orwq
title: 64PE398_ST27C26.xlsx
DOI: 10.25850/nioz/7b.b.s/kqafkg
title: 64PE398_ST27_allcasts.xlsx
DOI: 10.25850/nioz/7b.b.s/lahelo
title: 64PE398_ST28C01.xlsx
DOI: 10.25850/nioz/7b.b.s/wpeqau
title: 64PE398_ST32C01.xlsx
DOI: 10.25850/nioz/7b.b.s/mtsi4g
title: 64PE398_ST37C01.xlsx
DOI: 10.25850/nioz/7b.b.s/aczfgf
title: 64PE398_ST38C01.xlsx
DOI: 10.25850/nioz/7b.b.s/0xi62s
title: 64PE398_ST39C01.xlsx
DOI: 10.25850/nioz/7b.b.s/ikn0tf
title: 64PE398_ST40C01.xlsx
DOI: 10.25850/nioz/7b.b.s/rkxbr9
title: 64PE398_ST42C21.xlsx
DOI: 10.25850/nioz/7b.b.s/mf0efn
title: 64PE398_ST44C01.xlsx
DOI: 10.25850/nioz/7b.b.s/ixayc1
title: 64PE398_ST45C01.xlsx
DOI: 10.25850/nioz/7b.b.s/xcgceq
title: 64PE398_ST46C01.xlsx
DOI: 10.25850/nioz/7b.b.s/cgv0c2
title: 64PE398_ST47C01.xlsx
DOI: 10.25850/nioz/7b.b.s/mrkygh
title: 64PE398_ST49C01.xlsx
DOI: 10.25850/nioz/7b.b.s/qkqujl
title: 64PE398_ST49C08.xlsx
DOI: 10.21227/xm4q-s210
title: Spoken Indian Language Identification

description: Spoken Indian Language Identifcation (9 languages, 8 different utterance lengths)
DOI: 10.21227/h3rh-gt81
title: Spoken Indian Language Identification

description: Spoken Indian Language Identifcation (9 languages, 8 different utterance lengths)
DOI: 10.5281/zenodo.2636779
title: Trace gas mixing ratios measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> The authors would highly appreciate to be contacted if the data is used for any purpose. We measured mixing ratios of CO, CO<sub>2</sub> and CH<sub>4</sub> with a PICARRO G2401 Gas Analyzer. Ozone (O<sub>3</sub>) mixing ratios were measured with a 2B Technology ozone monitor, model 205. We report five-minute averaged data cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017. The trace gas concentrations represent a large number of atmospheric processes that happen on different time scales. CO for example, has basically no sources other than combustion and can hence be used as tracer for air mass transport from regions with combustion activities (e.g., South Africa). CO has a lifetime of a few weeks. CO<sub>2</sub> and CH<sub>4</sub> are longer-lived trace gases which disperse globally. The data set shows that concentrations in the Northern Hemisphere are higher than in the Southern Hemisphere. Both trace gases are emitted by anthropogenic activities as well as natural sources. Over the cruise track, areas of the Southern Ocean were passed where these trace gases either outgas or are absorbed. Ozone is a secondary trace gas, meaning that it is formed in the atmosphere. It’s concentrations are relatively low. All trace gases data have been cleaned from exhaust gas influence. <strong>Dataset contents</strong> ACESPACE_trace_gas_concentration.csv, data file, comma-separated values data_file_header.txt, metadata, text README.md, metadata, text NaN values denote missing values because of e.g., ship exhaust contamination or instrument maintenance. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. We acknowledge the Swiss National Sciences Foundation grant no. 200021_169090 and the German Research Foundation (DFG) within SSP 1158 (Grant no. STR 453/12-1).
description: {"references": ["Schmale, J. et al. (2017) Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition. Sci. Data 4:170003 doi: 10.1038/sdata.2017.3", "Igor Pro. Version 6.37. WaveMetrics, Inc., Oregon, USA. https://www.wavemetrics.com/downloads/current/Igor%20Pro%206.3", "LabView. Martin Gysel-Beer. Paul Scherrer Institute, Switzerland."]}
DOI: 10.25959/5eeccb497aedf
title: Data from: Tidal Modulation of Antarctic Ice Shelf Melting
DOI: 10.5281/zenodo.2636763
title: Equivalent black carbon aerosol measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.

description: <strong>Dataset abstract</strong> The authors would highly appreciate to be contacted if the data is used for any purpose. We measured equivalent black carbon (eBC) with an aethalometer (model AE33, Magee Scientific) at a time resolution of one second during the Antarctic Circumnavigation Expedition (ACE). We report five-minute averaged data, cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017. The mass concentration of eBC, reported in ng m<sup>-3</sup>, reflects how far fossil fuel combustion or biomass burning contribute to the aerosol population over the Southern Ocean and between South Africa and Europe. Over the Southern Ocean there are no sources of eBC, except for ship emissions and (sub-)Antarctic station emissions, and hence an enhancement of eBC points towards long-range influence from Africa, Australia, New Zealand and South America. When plotted against latitude, eBC concentrations drop south of 60°S, indicating a more pristine environment. Elevated concentration around the equator are likely influenced by biomass burning in tropical Africa. <strong>Dataset contents</strong> ACESPACE_equivalent_black_carbon_aerosol.csv, data file, comma-separated values data_file_header.txt, metadata, text README.md, metadata, text The data file listed above contains five-minute averaged values of equivalent black carbon (eBC) measured during the Antarctic Circumnavigation Expedition. Timestamps are the end of the five-minute period over which the eBC values were averaged. Latitude and longitude are average values of the position of the measurement during the five-minute interval. NaN values of eBC denote missing values because of e.g., ship exhaust contamination, maintenance, instrument failure or signal noise levels exceeding 200 ng/m<sup>3</sup>. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period. <strong>Dataset license</strong> This equivalent black carbon dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/
description: The Antarctic Circumnavigation Expedition was made possible by funding from the Swiss Polar Institute and Ferring Pharmaceuticals. We acknowledge the Swiss National Sciences Foundation grant no. 200021_169090 and the German Research Foundation (DFG) within SSP 1158 (Grant no. STR 453/12-1).
description: {"references": ["AETHALOMETERloader (2013). Paul Scherrer Institute, Switzerland", "Schmale et al. (2017) Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition. Scientific Data volume 4, Article number: 170003. 10.1038/sdata.2017.3", "Igor Pro. Version 6.37. WaveMetrics, Inc., Oregon, USA. https://www.wavemetrics.com/downloads/current/Igor%20Pro%206.3"]}