cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i20705948v16n2p272
title: A New Technology Acceptance Model on Industry 4.0: A Firm Based Regional Analysis

description: The aim of this study is to reveal the factors affecting Industry 4.0 technology acceptance within the framework of a newly proposed Technology Acceptance Model (TAM) of medium and large-scale firms in Manisa and Izmir Organized Industrial Zones, which are important industrial centres in Turkey. Within the scope of the study, managers of 204 firms operating in Manisa and Izmir Organized Industrial Zones have been interviewed and the data obtained has been analysed by Structural Equation Modelling (SEM) within the scope of TAM. According to the findings, it has been found that perceived usability, perceived ease of use, perceived self-efficacy and financing factors affect attitude towards Industry 4.0 use. In addition, it has been concluded that the attitude towards the use of Industry 4.0 also affects the Industry 4.0 usage intention and the Industry 4.0 usage intention affects the usage behavior.
DOI: 10.1285/i20705948v16n2p294
title: Theory and Optimization of Generalized Maximum Intuitionistic Fuzzy Entropy Methods

description: The studying and formulating of the generalized maximum fuzzy entropy methods for Intuitionistic Fuzzy Entropy is the focus of this manuscript. The methods were constructed by finding two generalized maximum fuzzy entropy distributions as MinMaxFE and MaxMaxFE, which gives the least and the greatest values of the entropy based on membership function values. We define the optimization problem and study the existence of the solution subject to moment constraints through Lagrange multiplier method. Real life application of data sets in medical fields and in image processing is studied to show whether the developed method can be applied successfully in fuzzy data analysis, and the performance of these distributions is measured using chi-square, RMSE, MFE criteria.
DOI: 10.1285/i20705948v16n2p311
title: Trend Analysis and Change Point Detection of Winter Rice in North Bank Plains Zone (NBPZ) of Assam: A Non-parametric Approach

description: Assam is well-recognized for its rich genetic diversity of rice. The climatic condition and geographical location cause better production of rice in the past of Assam. Winter rice is the leading rice crop, accounting for a major portion of the total rice production in Assam and NBPZ is one of the main regions where most of its livelihood depends on agriculture. The purpose of the study is to analyze the trend of area, production, and productivity of winter rice and identify the change point for winter rice productivity in NBPZ of Assam. Analyzing over thirty years of the data for the NBPZ led to the detection of the change point for the productivity of winter rice. The study also revealed that the NBPZ is not limited to studying climate- resilient productivity of winter rice only, but there is a great need for the implementation of effective measures on crop production and productivity. Thus, the outcome of the study has believed to reveal a better implementable plan for the farmers for designing an efficient agricultural policy by the policy makers.
DOI: 10.1285/i20705948v16n2p323
title: Different views of the multivariate ranking problem

description: Multivariate ranking problems are characterized by the need of ordering C different items according to several different features. The multivariate nature of these problems makes them quite challenging and flexible multivariate statistical techniques are therefore required. In this study we focus on two different scenarios, where we need to rank C different populations. Under the first scenario, preliminary knowledge about the order of the populations is available, while under the second one no information is available. Two solutions, based on the Nonparametric combination (NPC) technique, are proposed to deal with these scenarios and two case studies are adopted to facilitate the comprehension of the methods and to highlights the main differences between the two considered multivariate ranking problems.
DOI: 10.1285/i20705948v16n2p339
title: Estimation of the Parameters of the Generalized Inverted Exponential Distribution with Progressive type I Interval Censored Data

description: In this article, we study estimation methodologies for parameters of a generalized inverted exponentialdistribution based on different estimation methods using progressively type I interval censored data. Inthis approach, besides conventional maximum likelihood estimation, mid-point method, probability plotmethod and method of moments are proposed for parameter estimation. To obtain maximum likelihood esti-mates, we use Newton-Raphson, expectation-maximization and stochastic expectation-maximization meth-ods. Moreover, the approximate confidence intervals of the parameters are obtained via the inverse of theobserved information matrix. In addition, percentile bootstrap technique is utilized to compute confidenceintervals. Numerical comparisons are presented of the proposed estimators using Monte Carlo simulation-s. To demonstrate the proposed methodology in a real-life scenario, survival times of guinea pigs injectedwith different doses of tubercle bacilli data is considered to show the applicability of the proposed methods.Finally, different methods for determining the inspection times and optimal censoring planes are studied.Estimation of the Parameters of the Generalized Inverted ExponentialDistribution with Progressive type I Interval Censored Data
DOI: 10.1285/i20705948v16n2p382
title: Survival analysis of random censoring with inverse Maxwell distribution: an application to guinea pigs data

description: In real-life situations, performing an experiment up to a certain period of time or getting the desired number of failures is time-consuming and costly. Many of the available observations remain censored and only give the survival information of testing units up to a noted time and not about the exact failure times. In this article, the inverse Maxwell distribution having an upside-down hazard rate is considered a survival lifetime model. The censoring time is also assumed to follow the inverse Maxwell distribution with a different parameter. The probability of failure of an item before censoring and expected and observed time on the test is derived from a random censoring scheme. The maximum likelihood estimators with their confidence intervals for the parameters are obtained for a randomly censored setup. The Bayes estimators are also obtained by taking the inverted gamma distribution as a prior under squared error loss function. In Bayesian analysis, the two techniques i.e. Markov Chain Monte Carlo and Tierney-Kadane approximation methods are used for estimation purposes. For checking the performances of proposed estimators, we performed an extensive simulation study. A real data, guinea pigs, is analyzed to support the proposed study.
DOI: 10.15151/esrf-es-1345035204
title: Interfacial spin transport effects in single-crystalline epitaxial Fe/Rh bilayers

description: The generation of pure spin currents via spin pumping in magnetic bilayers is one of the key effects for efficient spin-to-charge conversion. This project focuses on a combination of epitaxial Fe films as spin source and Rh films as spin sink. Our FMR studies show a spin pumping efficiency in Fe/Rh comparable to the one of Fe/Pt which opens a great potential for spin-to-charge conversion application including THz emission. Combining XMCD, ferromagnetic resonance and magnetometry, we will study the magnetic proximity effect on the interfacial spin transport and spin dynamics. Using XMCD, we will investigate the changes of orbital and spin magnetic moments due to interface polarization and alloying of Rh films grown on single crystalline Fe as a function of film thickness and growth conditions. This information will be correlated with electrical and spin transport properties of Fe/Rh bilayers (spin pumping), g-factor, magnetic anisotropy, damping and THz emission measurements.
DOI: 10.1285/i20705948v16n2p410
title: A comparative study on repeated measurements data in the presence of missing data

description: The occurrence of missing observations is nearly unavoidable in longitudi- nal studies where repeated measurements are taken over time on the same subject who may miss appointments or drop out during the study period. In this article, we use the Gaussian estimating objective function to esti- mate the regression and correlation parameters and handle missing data using multiple imputation. The estimation of these parameters is carried out simultaneously using the iterative Newton-Raphson algorithm and the expectation-maximization algorithm. These ideas are implemented using two real data sets and both algorithms showed comparable results with respect to the standard errors of the parameters of interest.
DOI: 10.1285/i20705948v16n2p423
title: Confidence regions for simple correspondence analysis using the Cressie-Read family of divergence statistics

description: When examining the association between symmetrically associated categorical variables, correspondence analysis provides a visual means of identifying the structure of this association. An important and sometimes overlooked feature that can help the analyst determine whether those categories that provide a statistically significant contribution to the association is the confidence region. When constructing these regions, correspondence analysis traditionally (but not always) considers Pearson's chi-squared statistic as the core measure of association between the variables. Such a statistic is a special case of the Cressie-Read family of divergence statistics as is the log-likelihood ratio statistic, Freedman-Tukey statistic, and other such measures. Therefore, this paper will consider the construction of confidence regions in correspondence analysis where this family of divergence statistics is used as the measure of association. Doing so provides a means of simply constructing confidence regions for each category of a contingency table and allows for such regions to be constructed when log-ratio analysis (LRA) or the Hellinger distance decomposition (HDD) method is applied to the contingency table.
DOI: 10.1285/i20705948v16n2p449
title: A New Inverse Rayleigh Distribution with Applications of COVID-19 Data: Properties, Estimation Methods and Censored Sample

description: This paper aims at modelling the COVID-19 spread in the United Kingdom and the United States of America, by specifying an optimal statistical univariate model. A new lifetime distribution with three-parameters is introduced by a combination of inverse Rayleigh distribution and odd Weibull family of distributions to formulate the odd Weibull inverse Rayleigh (OWIR) distribution. Some of the mathematical properties of the OWIR distribution are discussed as linear representation, quantile, moments, function of moment production, hazard rate, stress-strength reliability, and order statistics. Maximum likelihood, maximum product spacing, and Bayesian estimation method are applied to estimate the unknown parameters of OWIR distribution. The parameters of the OWIR distribution are estimated under the progressive type-II censoring scheme with random removal. A numerical result of a Monte Carlo simulation is obtained to assess the use of estimation methods.
DOI: 10.22008/fk2/j6n3lm/e2xmfn
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_01.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_01.nc, date: 2017_04_01
DOI: 10.22008/fk2/j6n3lm/iziwt2
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_02.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_02.nc, date: 2017_04_02
DOI: 10.22008/fk2/j6n3lm/tgxzdo
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_03.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_03.nc, date: 2017_04_03
DOI: 10.22008/fk2/j6n3lm/yg35bs
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_04.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_04.nc, date: 2017_04_04
DOI: 10.22008/fk2/j6n3lm/ni2kev
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_06.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_06.nc, date: 2017_04_06
DOI: 10.22008/fk2/j6n3lm/ynz7am
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_05.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_05.nc, date: 2017_04_05
DOI: 10.22008/fk2/j6n3lm/jh7gan
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_07.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_07.nc, date: 2017_04_07
DOI: 10.22008/fk2/j6n3lm/ssa0ap
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_08.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_08.nc, date: 2017_04_08
DOI: 10.22008/fk2/j6n3lm/avhpmp
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_09.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_09.nc, date: 2017_04_09
DOI: 10.22008/fk2/j6n3lm/pbkti1
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_10.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_10.nc, date: 2017_04_10
DOI: 10.22008/fk2/j6n3lm/0kbxcr
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_11.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_11.nc, date: 2017_04_11
DOI: 10.22008/fk2/j6n3lm/o2qazp
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_12.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_12.nc, date: 2017_04_12
DOI: 10.22008/fk2/j6n3lm/oyfjjv
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_13.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_13.nc, date: 2017_04_13
DOI: 10.1285/i20705948v16n2p473
title: Stochastic analysis of COVID19 impact on birth life expectation

description: This framework consists in an application of an ARIMA model in order to analyze the impact of COVID19 pandemic on the birth life expectation process with a Impulse Response Function. More precisely we model the process, then we emulate the shock observed on this process to check if it is stable and trend reverting. In this way we decompose the time series of the birth life expectation in a deterministic trend component and a residual part where we will model with an ARIMA model with order 2 in auto-regressive component and with order 1 for the integration component. We will show that residual could be considered as white noise and, then, that the process is estimated to be stable and trend reverting in few years. Finally we compare forecast of the model without shock with the the one with shock. Then, we complete the comparison looking at the observed birth life expectation in order to see if the realty is following the theoretical framework.
DOI: 10.22008/fk2/j6n3lm/ns3ym0
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_14.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_14.nc, date: 2017_04_14
DOI: 10.22008/fk2/j6n3lm/dyb9sb
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_15.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_15.nc, date: 2017_04_15
DOI: 10.22008/fk2/j6n3lm/v1u0qu
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_16.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_16.nc, date: 2017_04_16
DOI: 10.22008/fk2/j6n3lm/jdeepl
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_17.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_17.nc, date: 2017_04_17
DOI: 10.22008/fk2/j6n3lm/7eqedc
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_18.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_18.nc, date: 2017_04_18
DOI: 10.22008/fk2/j6n3lm/h4rzfb
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_19.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_19.nc, date: 2017_04_19
DOI: 10.22008/fk2/j6n3lm/zxdo1x
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_20.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_20.nc, date: 2017_04_20
DOI: 10.22008/fk2/j6n3lm/iyqrp9
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_21.nc, date: 2017_04_21
DOI: 10.22008/fk2/j6n3lm/m4yt2t
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_22.nc, date: 2017_04_22
DOI: 10.22008/fk2/j6n3lm/f4lnjm
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_23.nc, date: 2017_04_23
DOI: 10.22008/fk2/j6n3lm/0fmffo
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_25.nc, date: 2017_04_25
DOI: 10.22008/fk2/j6n3lm/1wg8sr
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_24.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_24.nc, date: 2017_04_24
DOI: 10.22008/fk2/j6n3lm/ok6qtd
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_26.nc, date: 2017_04_26
DOI: 10.22008/fk2/j6n3lm/6tkhia
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_27.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_27.nc, date: 2017_04_27
DOI: 10.22008/fk2/j6n3lm/elvpin
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_28.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_28.nc, date: 2017_04_28
DOI: 10.22008/fk2/j6n3lm/3bvkir
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_30.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_30.nc, date: 2017_04_30
DOI: 10.22008/fk2/j6n3lm/atk1ue
title: SICEv2.3.2_FransJosefLand_1000m_2017_04_29.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_04_29.nc, date: 2017_04_29
DOI: 10.22008/fk2/j6n3lm/zarqqd
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_01.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_01.nc, date: 2017_05_01
DOI: 10.22008/fk2/j6n3lm/7cdvkn
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_02.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_02.nc, date: 2017_05_02
DOI: 10.22008/fk2/j6n3lm/6gfabx
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_03.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_03.nc, date: 2017_05_03
DOI: 10.22008/fk2/j6n3lm/rn1nio
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_04.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_04.nc, date: 2017_05_04
DOI: 10.22008/fk2/j6n3lm/y6wxa6
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_05.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_05.nc, date: 2017_05_05
DOI: 10.22008/fk2/j6n3lm/8z139h
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_07.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_07.nc, date: 2017-05-07
DOI: 10.22008/fk2/j6n3lm/bfgbjb
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_06.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_06.nc, date: 2017_05_06
DOI: 10.22008/fk2/j6n3lm/ml4ezq
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_08.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_08.nc, date: 2017-05-08
DOI: 10.22008/fk2/j6n3lm/6laoco
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_09.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_09.nc, date: 2017-05-09
DOI: 10.22008/fk2/j6n3lm/pd39wi
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_10.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_10.nc, date: 2017-05-10
DOI: 10.22008/fk2/j6n3lm/cypecf
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_11.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_11.nc, date: 2017-05-11
DOI: 10.1285/i20705948v16n2p487
title: New Wald-Type Estimation Procedures for fitting Structural Measurement Error Model

description: This article proposes a new estimation method to fit the structural regression model when the variables are subject to errors. The new estimation method is the extension of the Wald estimation method and involves iterative process. Several Monte Carlo simulation experiments were used to study the performance of the proposed estimators. The results were compared with the classical Wald estimation method in terms of its root mean square error (RMSE). In addition, an application for examining the relationships between Jordan’s national gross domestic product (GDP) and its human development index (HDI) was presented. Numerical results showed that the GDP and HDI have a strong positive and significant correlation. Moreover, the proposed procedures with different subgroup sizes (r =3 and r =4) gave more accurate estimators than the classical estimation methods in fitting the relationships between GDP and HDI.
DOI: 10.22008/fk2/j6n3lm/yb2gtc
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_12.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_12.nc, date: 2017-05-12
DOI: 10.22008/fk2/j6n3lm/xihz18
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_13.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_13.nc, date: 2017-05-13
DOI: 10.22008/fk2/j6n3lm/mmcbt4
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_14.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_14.nc, date: 2017-05-14
DOI: 10.22008/fk2/j6n3lm/zigush
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_15.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_15.nc, date: 2017-05-15
DOI: 10.22008/fk2/j6n3lm/g0gycq
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_16.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_16.nc, date: 2017-05-16
DOI: 10.22008/fk2/j6n3lm/57nkjg
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_17.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_17.nc, date: 2017-05-17
DOI: 10.22008/fk2/j6n3lm/qqwm3v
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_19.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_19.nc, date: 2017-05-19
DOI: 10.22008/fk2/j6n3lm/vjmn9w
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_18.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_18.nc, date: 2017-05-18
DOI: 10.22008/fk2/j6n3lm/5kvo2x
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_20.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_20.nc, date: 2017-05-20
DOI: 10.22008/fk2/j6n3lm/prdp3o
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_21.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_21.nc, date: 2017-05-21
DOI: 10.22008/fk2/j6n3lm/her2rl
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_22.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_22.nc, date: 2017-05-22
DOI: 10.22008/fk2/j6n3lm/4ijlfp
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_23.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_23.nc, date: 2017-05-23
DOI: 10.22008/fk2/j6n3lm/6dwp8y
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_24.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_24.nc, date: 2017-05-24
DOI: 10.22008/fk2/j6n3lm/gma8pv
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_25.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_25.nc, date: 2017-05-25
DOI: 10.22008/fk2/j6n3lm/xmtj6u
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_26.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_26.nc, date: 2017-05-26
DOI: 10.22008/fk2/j6n3lm/oamxyg
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_27.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_27.nc, date: 2017-05-27
DOI: 10.22008/fk2/j6n3lm/kso9z3
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_28.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_28.nc, date: 2017-05-28
DOI: 10.22008/fk2/j6n3lm/ivqoyd
title: SICEv2.3.2_FransJosefLand_1000m_2017_05_30.nc

description: SICEv2.3.2 output: SICEv2.3.2_FransJosefLand_1000m_2017_05_30.nc, date: 2017-05-30