Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2015S18, deployed during POLARSTERN cruise ANT-XXX/2 (PS89)

Snow height was measured by the Snow Depth Buoy 2015S18, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXX/2 (PS89). The resulting time series describes the evolution of snow depth as a function of place and time between 2015-01-03 and 2015-01-18 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S13, deployed during the CryoVEx2014 field campaign

Snow height was measured by the Snow Depth Buoy 2014S13, an autonomous platform, drifting on Arctic sea ice, deployed during the CryoVEx2014 field campaign. The resulting time series describes the evolution of snow height as a function of place and time between 2014-03-30 and 2014-07-20 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on multi year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S1, deployed during Antarctic Fast Ice Network 2013 (AFIN 2013)

Snow height was measured by the Snow Depth Buoy 2013S1, an autonomous platform, installed close to Neumayer III Base, Antarctic during Antarctic Fast Ice Network 2013 (AFIN 2013). The resulting time series describes the evolution of snow height as a function of place and time between 2013-02-11 and 2013-04-29 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on the ice shelf. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S3, deployed at the Barneo ice camp 2013

Snow height was measured by the Snow Depth Buoy 2013S3, an autonomous platform, drifting on Arctic sea ice. This buoy was deployed at the Barneo ice camp 2013. The resulting time series describes the evolution of snow height as a function of place and time between 2013-04-09 and 2013-06-13 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on multi year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S4, deployed during SIZONet 2013

Snow height was measured by the Snow Depth Buoy 2013S4, an autonomous platform, installed on land-fast sea ice off Barrow, Alaska during SIZONet 2013. The resulting time series describes the evolution of snow height as a function of place and time between 2013-04-09 and 2013-06-28 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on land-fast sea ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow height, because no initial readings of absolute snow height are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S6, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S6, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-06-24 and 2013-09-27 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S8, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S8, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-09 and 2014-01-05 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S7, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S7, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-06 and 2013-09-13 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S15, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87)

Snow height was measured by the Snow Depth Buoy 2014S15, an autonomous platform, drifting on Arctic sea ice, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-08-29 and 2014-12-31 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). The buoy was installed on multi year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S17, deployed during POLARSTERN cruise ANT-XXX/2 (PS89)

Snow height was measured by the Snow Depth Buoy 2014S17, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXX/2 (PS89). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-12-20 and 2015-02-01 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). In this data set, diurnal variations occur in the data set, although the sonic readings were compensated for temperature changes. Records without any snow depth may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S24, deployed during Antarctic Fast Ice Network 2014 (AFIN 2014)

Snow height was measured by the Snow Depth Buoy 2014S24, an autonomous platform, installed close to Neumayer III Base, Antarctic during Antarctic Fast Ice Network 2014 (AFIN 2014). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-03-07 and 2014-05-16 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on the ice shelf. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Desigualdade social entre as crianças menores de 5 anos em diferentes regiões mundiais

Introdução: A Organização Mundial da Saúde indica que a prevalência do déficit de altura tem diminuído no planeta nas últimas décadas, pouco se sabe ainda sobre os fatores associados a este declínio ou sua associação com a desigualdade social. Objetivo: Descrever a evolução do déficit de altura e da desigualdade socioeconômica em diferentes regiões do mundo. Métodos: A pesquisa foi baseada em dados secundários provenientes do programa Demografic Health Surveys DHS de 6 sub-regiões do mundo representando 24 países em um total de 48 pesquisas na década de 90 e na primeira década do século 21 com 377.151 crianças menores de 5 anos. Foi considerada como variável de interesse o Déficit de altura para idade considerado como a ocorrência deste índice inferior a -2 escore Z da distribuição de referência WHO-2006. Foram imputados através de modelo de regressão os valores faltantes das variáveis água para beber, esgoto sanitário e escolaridade materna. Foi estimado o Índice de Concentração para as variáveis déficit de altura, educação materna deficiente, água para beber insegura, esgoto domiciliar deficiente e ocorrência de doenças, tendo como variável de ranqueamento o Índice de Riqueza. Dados do poder de paridade de compra fornecidos pelo Banco Mundial foram utilizados para verificar as diferenças na evolução da desnutrição. Resultados: Nessa análise acerca da evolução da desigualdade socioeconômica do déficit de altura para idade em países em desenvolvimento constatou-se que: a) a prevalência do déficit de altura para idade decresceu em 87 por cento dos países; b) apenas 8 países (33 por cento ) aumentaram a diferença entre prevalência do déficit de altura nos quintos extremos c) quatorze países (58 por cento ) evoluíram com diminuição do déficit de altura e aumento do índice de concentração; d) Dois países que diminuíram a o déficit de altura e a desigualdade tinham os menores valores de escolaridade materna deficiente; e) 13 países (93 por cento ) daqueles que diminuíram déficit mas aumentaram a desigualdade possuíam indicadores de vulnerabilidade infantil deficientes. Conclusões: Os países em desenvolvimento apresentam redução no déficit de altura em crianças menores de 5 anos. A diminuição da desigualdade na riqueza e na escolaridade materna deficiente explicaram maior parte da melhoria da desigualdade do déficit de altura para idade.

Alternativas de análise para experimentos G × E multiatributo

Geralmente, nos experimentos genótipo por ambiente (G × E) é comum observar o comportamento dos genótipos em relação a distintos atributos nos ambientes considerados. A análise deste tipo de experimentos tem sido abordada amplamente para o caso de um único atributo. Nesta tese são apresentadas algumas alternativas de análise considerando genótipos, ambientes e atributos simultaneamente. A primeira, é baseada no método de mistura de máxima verossimilhança de agrupamento - Mixclus e a análise de componentes principais de 3 modos - 3MPCA, que permitem a análise de tabelas de tripla entrada, estes dois métodos têm sido muito usados na área da psicologia e da química, mas pouco na agricultura. A segunda, é uma metodologia que combina, o modelo de efeitos aditivos com interação multiplicativa - AMMI, modelo eficiente para a análise de experimentos (G × E) com um atributo e a análise de procrustes generalizada, que permite comparar configurações de pontos e proporcionar uma medida numérica de quanto elas diferem. Finalmente, é apresentada uma alternativa para realizar imputação de dados nos experimentos (G × E), pois, uma situação muito frequente nestes experimentos, é a presença de dados faltantes. Conclui-se que as metodologias propostas constituem ferramentas úteis para a análise de experimentos (G × E) multiatributo.

Imputação de dados em experimentos multiambientais: novos algoritmos utilizando a decomposição por valores singulares

As análises biplot que utilizam os modelos de efeitos principais aditivos com inter- ação multiplicativa (AMMI) requerem matrizes de dados completas, mas, frequentemente os ensaios multiambientais apresentam dados faltantes. Nesta tese são propostas novas metodologias de imputação simples e múltipla que podem ser usadas para analisar da- dos desbalanceados em experimentos com interação genótipo por ambiente (G×E). A primeira, é uma nova extensão do método de validação cruzada por autovetor (Bro et al, 2008). A segunda, corresponde a um novo algoritmo não-paramétrico obtido por meio de modificações no método de imputação simples desenvolvido por Yan (2013). Também é incluído um estudo que considera sistemas de imputação recentemente relatados na literatura e os compara com o procedimento clássico recomendado para imputação em ensaios (G×E), ou seja, a combinação do algoritmo de Esperança-Maximização com os modelos AMMI ou EM-AMMI. Por último, são fornecidas generalizações da imputação simples descrita por Arciniegas-Alarcón et al. (2010) que mistura regressão com aproximação de posto inferior de uma matriz. Todas as metodologias têm como base a decomposição por valores singulares (DVS), portanto, são livres de pressuposições distribucionais ou estruturais. Para determinar o desempenho dos novos esquemas de imputação foram realizadas simulações baseadas em conjuntos de dados reais de diferentes espécies, com valores re- tirados aleatoriamente em diferentes porcentagens e a qualidade das imputações avaliada com distintas estatísticas. Concluiu-se que a DVS constitui uma ferramenta útil e flexível na construção de técnicas eficientes que contornem o problema de perda de informação em matrizes experimentais.

Multiple imputation for body mass index: lessons from the Australian Longitudinal Study on Women's Health

In large epidemiological studies missing data can be a problem, especially if information is sought on a sensitive topic or when a composite measure is calculated from several variables each affected by missing values. Multiple imputation is the method of choice for 'filling in' missing data based on associations among variables. Using an example about body mass index from the Australian Longitudinal Study on Women's Health, we identify a subset of variables that are particularly useful for imputing values for the target variables. Then we illustrate two uses of multiple imputation. The first is to examine and correct for bias when data are not missing completely at random. The second is to impute missing values for an important covariate; in this case omission from the imputation process of variables to be used in the analysis may introduce bias. We conclude with several recommendations for handling issues of missing data. Copyright (C) 2004 John Wiley Sons, Ltd.