cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.35078/f016oy/o5gsfg
title: Dorvino José da Silva A.jpg

description: :unav
DOI: 10.35078/f016oy/8lz2qq
title: Erotildes P.Nascimento B.jpg

description: :unav
DOI: 10.35078/f016oy/nn3q6h
title: Capão Grande B.jpg

description: :unav
DOI: 10.35078/f016oy/thjn9c
title: Nélson Amâncio de Melo B.jpg

description: :unav
DOI: 10.35078/f016oy/aj4ty7
title: Sete Voltas A.jpg

description: :unav
DOI: 10.35078/f016oy/keiedw
title: Osvaldo Borges Araújo A.jpg

description: :unav
DOI: 10.35078/f016oy/q50mt3
title: Olhos D'Água II.B.jpg

description: :unav
DOI: 10.35078/f016oy/hlqodc
title: Felipe Augusto Pires A.jpg

description: :unav
DOI: 10.35078/f016oy/bzvxkn
title: Posses A.jpg

description: :unav
DOI: 10.35078/f016oy/kiskp1
title: Eucalípito A.jpg

description: :unav
DOI: 10.35078/f016oy/mzok4k
title: Sebastião Marques Borges A.jpg

description: :unav
DOI: 10.35078/f016oy/slcnp3
title: Palmital B.jpg

description: :unav
DOI: 10.35078/f016oy/ysg3y4
title: Cafundó A.B.jpg

description: :unav
DOI: 10.35078/f016oy/1nm7l7
title: Soledade B.jpg

description: :unav
DOI: 10.35078/f016oy/6xnhgh
title: Zagaia B.jpg

description: :unav
DOI: 10.35078/f016oy/t8kw0x
title: Santo Antônio A.jpg

description: :unav
DOI: 10.35078/f016oy/rgftmx
title: Sebastião L.Mota A.jpg

description: :unav
DOI: 10.35078/f016oy/vl8jna
title: Genésio C.Cunha B.jpg

description: :unav
DOI: 10.35078/f016oy/9nlizn
title: Perobas B.jpg

description: :unav
DOI: 10.35078/f016oy/wczalt
title: Cipó A.jpg

description: :unav
DOI: 10.35078/f016oy/um8cib
title: Córrego Retirinho B.jpg

description: :unav
DOI: 10.35078/f016oy/xc0jx7
title: Jataí B.jpg

description: :unav
DOI: 10.35078/f016oy/6exhqe
title: Pedro Zandonaide B.jpg

description: :unav
DOI: 10.35078/f016oy/ftqwpr
title: João Batista Honório B.jpg

description: :unav
DOI: 10.35078/f016oy/la9lfx
title: José Carneiro Filho B.jpg

description: :unav
DOI: 10.35078/f016oy/fwdfvn
title: Jataí A.jpg

description: :unav
DOI: 10.35078/f016oy/fzbxj9
title: Vale das Pedras A.jpg

description: :unav
DOI: 10.35078/f016oy/jr68ys
title: Açudes I.A.jpg

description: :unav
DOI: 10.35078/f016oy/edypzd
title: Samambaia A.jpg

description: :unav
DOI: 10.35078/f016oy/bo1bhk
title: Santa Bárbara B.jpg

description: :unav
DOI: 10.35078/f016oy/wtlm11
title: Santo Antônio B.jpg

description: :unav
DOI: 10.35078/f016oy/e6wrcv
title: Bananal Pov.B.jpg

description: :unav
DOI: 10.35078/f016oy/hly0qk
title: Tavares B.jpg

description: :unav
DOI: 10.35078/f016oy/qi7dcu
title: Patos A.jpg

description: :unav
DOI: 10.35078/f016oy/neifpd
title: Pedra Grande A.jpg

description: :unav
DOI: 10.35078/f016oy/m479ui
title: Valinhos B.jpg

description: :unav
DOI: 10.35078/f016oy/xjt9yq
title: Valdemar Vitorino Alves B.jpg

description: :unav
DOI: 10.35078/f016oy/eha6ue
title: Osvaldo Borges Araújo B.jpg

description: :unav
DOI: 10.35078/f016oy/hdb74j
title: Barreirinho A.B.jpg

description: :unav
DOI: 10.35078/f016oy/uwtarv
title: Dalva Cândida de Melo B.jpg

description: :unav
DOI: 10.35078/f016oy/okq8my
title: Açude A.jpg

description: :unav
DOI: 10.35078/f016oy/6qkldb
title: Mata do Paiol B.jpg

description: :unav
DOI: 10.35078/f016oy/uwvmom
title: Manoel Teodoro Oliveira A.jpg

description: :unav
DOI: 10.35078/f016oy/ch6c3g
title: Cafundó B.jpg

description: :unav
DOI: 10.35078/f016oy/zwtdln
title: Córrego da Mata B.jpg

description: :unav
DOI: 10.35078/f016oy/znsel6
title: Macaúba B.jpg

description: :unav
DOI: 10.35078/f016oy/77h59y
title: Fundão B.jpg

description: :unav
DOI: 10.35078/f016oy/utt24i
title: Antônio Batista A.jpg

description: :unav
DOI: 10.35078/f016oy/mkqomg
title: Vitorinos B.jpg

description: :unav
DOI: 10.35078/f016oy/rfsgb5
title: Usina Santo Antônio B.jpg

description: :unav
DOI: 10.35078/f016oy/byr5g8
title: Ponta Grande B.jpg

description: :unav
DOI: 10.35078/f016oy/l6tizo
title: Mata A.jpg

description: :unav
DOI: 10.35078/f016oy/bgv4by
title: Otelvino Gomes Menezes B.jpg

description: :unav
DOI: 10.35078/f016oy/1o83yr
title: Córrego Marimbondinho A.jpg

description: :unav
DOI: 10.35078/f016oy/ttnuvx
title: Agenor José Lacerda A.jpg

description: :unav
DOI: 10.35078/f016oy/wphfex
title: Osvaldo Borges Araújo Faz.A.jpg

description: :unav
DOI: 10.35078/f016oy/cpifyo
title: Cassiano G.de Souza B.jpg

description: :unav
DOI: 10.35078/f016oy/ocagj0
title: Córrego do Atoleiro A.jpg

description: :unav
DOI: 10.35078/f016oy/o8nqit
title: Açude B.jpg

description: :unav
DOI: 10.35078/f016oy/vucgto
title: Santa Maria A.jpg

description: :unav
DOI: 10.35078/f016oy/yn8mbs
title: Cafundó A.jpg

description: :unav
DOI: 10.35078/f016oy/yhxasg
title: Pedroso B.jpg

description: :unav
DOI: 10.35078/f016oy/zncqut
title: Eucalípito B.jpg

description: :unav
DOI: 10.35078/f016oy/1kon5o
title: Cór.do Paiol A.jpg

description: :unav
DOI: 10.35078/f016oy/fiu4ew
title: Delfino Batista Oliveira B.jpg

description: :unav
DOI: 10.35078/f016oy/dwkp7u
title: Ponte Queimada B.jpg

description: :unav
DOI: 10.35078/f016oy/mkw8z3
title: Capão Alto A.jpg

description: :unav
DOI: 10.35078/f016oy/mwmqev
title: Euclides V. Rezende B.jpg

description: :unav
DOI: 10.35078/f016oy/u38bhx
title: Alencar A.Souza B.jpg

description: :unav
DOI: 10.35078/f016oy/idolds
title: Agenor José Lacerda B.jpg

description: :unav
DOI: 10.35078/f016oy/knulvr
title: Taquaral A.jpg

description: :unav
DOI: 10.35078/f016oy/qim55x
title: Vicente T.Oliveira A.jpg

description: :unav
DOI: 10.35078/f016oy/j7z3ge
title: Jerônimo Borges B.jpg

description: :unav
DOI: 10.35078/f016oy/zjkssy
title: José V.de Paula A.jpg

description: :unav
DOI: 10.35078/f016oy/gvcbsi
title: Lapa B.jpg

description: :unav
DOI: 10.35078/f016oy/oa67uy
title: Urias Abelardo Leite A.jpg

description: :unav
DOI: 10.35078/f016oy/wlsk9t
title: Jaime Teixeira A.jpg

description: :unav
DOI: 10.35078/f016oy/cy0pgc
title: Bela Vista A.jpg

description: :unav
DOI: 10.35078/f016oy/xsvsz0
title: Pai Joaquim B.jpg

description: :unav
DOI: 10.35078/f016oy/hwbtbu
title: Alcedino Fajardo Mello A.jpg

description: :unav
DOI: 10.35078/f016oy/ix5uak
title: Córrego Cercadinho B.jpg

description: :unav
DOI: 10.35078/f016oy/qjg38s
title: Córrego Capão dos Porcos A.jpg

description: :unav
DOI: 10.35078/f016oy/4kbqnz
title: Bocaina B.jpg

description: :unav
DOI: 10.35078/f016oy/0b87mq
title: Boa Vista A.jpg

description: :unav
DOI: 10.35078/f016oy/56xni8
title: José Palhares A.jpg

description: :unav
DOI: 10.35078/f016oy/tbwuoe
title: Córrego Retirinho A.jpg

description: :unav
DOI: 10.35078/f016oy/h4zhdo
title: Orlando Pereira A.jpg

description: :unav
DOI: 10.35078/f016oy/b5yao4
title: Sete Voltas B.jpg

description: :unav
DOI: 10.35078/f016oy/tfqscl
title: José Gomes Menezes B.jpg

description: :unav
DOI: 10.35078/f016oy/gdn2mp
title: Barreirinho B.jpg

description: :unav
DOI: 10.35078/f016oy/esxecv
title: Jerônimo Valeriano B.jpg

description: :unav
DOI: 10.35078/f016oy/m5utfr
title: Fundão A.jpg

description: :unav
DOI: 10.35078/f016oy/ibcn4s
title: Velha A.B.jpg

description: :unav
DOI: 10.35078/f016oy/llihc0
title: José Palhares B.jpg

description: :unav
DOI: 10.35078/f016oy/cenyzj
title: Ventania B.jpg

description: :unav
DOI: 10.35078/f016oy/bdg6ac
title: Euclides V. Rezende A.jpg

description: :unav
DOI: 10.35078/f016oy/yaqb9q
title: Rifaininha B.jpg

description: :unav
DOI: 10.35078/f016oy/m2dajo
title: Santo Antônio B.B.jpg

description: :unav
DOI: 10.35078/f016oy/kadqvy
title: José Gomes Menezes A.jpg

description: :unav
DOI: 10.35078/f016oy/tpdg7k
title: Santo Antônio Vila.A.jpg

description: :unav
DOI: 10.35078/f016oy/geb5af
title: José G.de Souza B.jpg

description: :unav
DOI: 10.35078/f016oy/dmfdip
title: José Cardoso B.jpg

description: :unav
DOI: 10.35078/f016oy/nypg6y
title: Cór.do Paiol B.jpg

description: :unav
DOI: 10.35078/f016oy/pvwkoq
title: Capão da Onça B.jpg

description: :unav
DOI: 10.35078/f016oy/0jrjfj
title: Acampamento Resa B.jpg

description: :unav
DOI: 10.35078/f016oy/ctlbld
title: Cascalho A.jpg

description: :unav
DOI: 10.35078/f016oy/fhwo0l
title: Córrego do Tapera A.jpg

description: :unav
DOI: 10.35078/f016oy/s7iswe
title: São José A.jpg

description: :unav
DOI: 10.35078/f016oy/kulwig
title: Ponte Queimada A.jpg

description: :unav
DOI: 10.35078/f016oy/dbb70s
title: Barcelos B.jpg

description: :unav
DOI: 10.35078/f016oy/683eif
title: DICIONARIO_KARDEX_PROGRAMA_CONTROLE_DOENÇA_CHAGAS.pdf

description: Dicionário para interpretação da ficha epidemiologica (Kardex)
DOI: 10.6084/m9.figshare.19439010
title: GEE-Assisted Forward Regression for Spatial Latent Variable Models

description: Multivariate spatial data, where multiple responses are recorded at a set of spatial locations, are widely collected in many disciplines. One common approach for analyzing such data is spatial generalized linear latent variable models (spatial GLLVMs), where the latent variables are used to model both the spatial correlation between locations and correlations between responses. However, inference such as variable selection for spatial GLLVMs is computationally demanding, as the marginal likelihood involves a high-dimensional and often intractable integral. To overcome this, we propose to use spatial generalized estimating equations (GEEs) to perform fast, GEE-assisted forward regression for spatial GLLVMs. Focusing on counts and nonnegative continuous responses, we use spatial GEEs to build a forward solution path by choosing the candidate variable which maximizes a score statistic at each point on the path. A model is then selected from this path based on a modified score information criterion. The proposed approach is computationally efficient, relying only on GEEs which are quick to update, coupled with a novel theoretical result linking the coefficients from spatial GEEs to that of spatial GLLVMs. We show that the proposed approach can asymptotically identify all truly important nonzero predictors in the underlying spatial GLLVM. Simulations demonstrate that, when the data are generated from a sparse spatial GLLVM, GEE-assisted forward regression performs well at recovering this sparsity, while taking only a fraction of the computation time required to fit just a single (saturated) spatial GLLVM. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19439010.v2
title: GEE-Assisted Forward Regression for Spatial Latent Variable Models

description: Multivariate spatial data, where multiple responses are recorded at a set of spatial locations, are widely collected in many disciplines. One common approach for analyzing such data is spatial generalized linear latent variable models (spatial GLLVMs), where the latent variables are used to model both the spatial correlation between locations and correlations between responses. However, inference such as variable selection for spatial GLLVMs is computationally demanding, as the marginal likelihood involves a high-dimensional and often intractable integral. To overcome this, we propose to use spatial generalized estimating equations (GEEs) to perform fast, GEE-assisted forward regression for spatial GLLVMs. Focusing on counts and nonnegative continuous responses, we use spatial GEEs to build a forward solution path by choosing the candidate variable which maximizes a score statistic at each point on the path. A model is then selected from this path based on a modified score information criterion. The proposed approach is computationally efficient, relying only on GEEs which are quick to update, coupled with a novel theoretical result linking the coefficients from spatial GEEs to that of spatial GLLVMs. We show that the proposed approach can asymptotically identify all truly important nonzero predictors in the underlying spatial GLLVM. Simulations demonstrate that, when the data are generated from a sparse spatial GLLVM, GEE-assisted forward regression performs well at recovering this sparsity, while taking only a fraction of the computation time required to fit just a single (saturated) spatial GLLVM. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19497645
title: Bayesian Semiparametric Covariate Informed Multivariate Density Deconvolution

description: Estimating the marginal and joint densities of the long-term average intakes of different dietary components is an important problem in nutritional epidemiology. Since these variables cannot be directly measured, data are usually collected in the form of 24-hour recalls of the intakes. The problem of estimating the density of the latent long-term average intakes from their observed but error contaminated recalls then becomes a problem of multivariate deconvolution of densities. The underlying densities could potentially vary with the subjects’ demographic characteristics such as sex, ethnicity, age, etc. The problem of density deconvolution in the presence of associated precisely measured covariates has, however, never been considered before, not even in the univariate setting. We present a flexible Bayesian semiparametric approach to covariate informed multivariate deconvolution. Building on recent advances on copula deconvolution and conditional tensor factorization techniques, our proposed method not only allows the joint and the marginal densities to vary flexibly with the associated predictors but also allows automatic selection of the most influential predictors. Importantly, the method also allows the density of interest and the density of the measurement errors to vary with potentially different sets of predictors. We design Markov chain Monte Carlo algorithms that enable efficient posterior inference, appropriately accommodating uncertainty in all aspects of our analysis. The empirical efficacy of the proposed method is illustrated through simulation experiments. Its practical utility is demonstrated in the afore-described nutritional epidemiology applications in estimating covariate adjusted long term intakes of different dietary components. An important by-product of the approach is a solution to covariate informed ordinary multivariate density estimation. Supplementary materials include substantive additional details and R codes are also available online.
DOI: 10.6084/m9.figshare.19497645.v1
title: Bayesian Semiparametric Covariate Informed Multivariate Density Deconvolution

description: Estimating the marginal and joint densities of the long-term average intakes of different dietary components is an important problem in nutritional epidemiology. Since these variables cannot be directly measured, data are usually collected in the form of 24-hour recalls of the intakes. The problem of estimating the density of the latent long-term average intakes from their observed but error contaminated recalls then becomes a problem of multivariate deconvolution of densities. The underlying densities could potentially vary with the subjects’ demographic characteristics such as sex, ethnicity, age, etc. The problem of density deconvolution in the presence of associated precisely measured covariates has, however, never been considered before, not even in the univariate setting. We present a flexible Bayesian semiparametric approach to covariate informed multivariate deconvolution. Building on recent advances on copula deconvolution and conditional tensor factorization techniques, our proposed method not only allows the joint and the marginal densities to vary flexibly with the associated predictors but also allows automatic selection of the most influential predictors. Importantly, the method also allows the density of interest and the density of the measurement errors to vary with potentially different sets of predictors. We design Markov chain Monte Carlo algorithms that enable efficient posterior inference, appropriately accommodating uncertainty in all aspects of our analysis. The empirical efficacy of the proposed method is illustrated through simulation experiments. Its practical utility is demonstrated in the afore-described nutritional epidemiology applications in estimating covariate adjusted long term intakes of different dietary components. An important by-product of the approach is a solution to covariate informed ordinary multivariate density estimation. Supplementary materials include substantive additional details and R codes are also available online.
DOI: 10.6084/m9.figshare.19439010.v1
title: GEE-assisted Forward Regression for Spatial Latent Variable Models

description: Multivariate spatial data, where multiple responses are recorded at a set of spatial locations, are widely collected in many disciplines. One common approach for analyzing such data is spatial generalized linear latent variable models (spatial GLLVMs), where the latent variables are used to model both the spatial correlation between locations and correlations between responses. However, inference such as variable selection for spatial GLLVMs is computationally demanding, as the marginal likelihood involves a high-dimensional and often intractable integral. To overcome this, we propose to use spatial generalized estimating equations (GEEs) to perform fast, GEE-assisted forward regression for spatial GLLVMs. Focusing on counts and non-negative continuous responses, we use spatial GEEs to build a forward solution path by choosing the candidate variable which maximizes a score statistic at each point on the path. A model is then selected from this path based on a modified score information criterion. The proposed approach is computationally efficient, relying only on GEEs which are quick to update, coupled with a novel theoretical result linking the coefficients from spatial GEEs to that of spatial GLLVMs. We show that the proposed approach can asymptotically identify all truly important non-zero predictors in the underlying spatial GLLVM. Simulations demonstrate that, when the data are generated from a sparse spatial GLLVM, GEE-assisted forward regression performs well at recovering this sparsity, while taking only a fraction of the computation time required to fit just a single (saturated) spatial GLLVM.
DOI: 10.6084/m9.figshare.19439008
title: Graded Matching for Large Observational Studies

description: Observational studies of causal effects often use multivariate matching to control imbalances in measured covariates. For instance, using network optimization, one may seek the closest possible pairing for key covariates among all matches that balance a propensity score and finely balance a nominal covariate, perhaps one with many categories. This is all straightforward when matching thousands of individuals, but requires some adjustments when matching tens or hundreds of thousands of individuals. In various senses, a sparser network—one with fewer edges—permits optimization in larger samples. The question is: What is the best way to make the network sparse for matching? A network that is too sparse will eliminate from consideration possible pairings that it should consider. A network that is not sparse enough will waste computation considering pairings that do not deserve serious consideration. We propose a new graded strategy in which potential pairings are graded, with a preference for higher grade pairings. We try to match with pairs of the best grade, incorporating progressively lower grade pairs only to the degree they are needed. In effect, only sparse networks are built, stored and optimized. Two examples are discussed, a small example with 1567 matched pairs from clinical medicine, and a slightly larger example with 22,111 matched pairs from economics. The method is implemented in an R package RBestMatch available at https://github.com/ruoqiyu/RBestMatch. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19439008.v1
title: Graded Matching for Large Observational Studies

description: Observational studies of causal effects often use multivariate matching to control imbalances in measured covariates. For instance, using network optimization, one may seek the closest possible pairing for key covariates among all matches that balance a propensity score and finely balance a nominal covariate, perhaps one with many categories. This is all straightforward when matching thousands of individuals, but requires some adjustments when matching tens or hundreds of thousands of individuals. In various senses, a sparser network—one with fewer edges—permits optimization in larger samples. The question is: What is the best way to make the network sparse for matching? A network that is too sparse will eliminate from consideration possible pairings that it should consider. A network that is not sparse enough will waste computation considering pairings that do not deserve serious consideration. We propose a new graded strategy in which potential pairings are graded, with a preference for higher grade pairings. We try to match with pairs of the best grade, incorporating progressively lower grade pairs only to the degree they are needed. In effect, only sparse networks are built, stored and optimized. Two examples are discussed, a small example with 1567 matched pairs from clinical medicine, and a slightly larger example with 22,111 matched pairs from economics. The method is implemented in an R package RBestMatch available at https://github.com/ruoqiyu/RBestMatch. Supplementary materials for this article are available online.
DOI: 10.15139/s3/6foshq
title: Ghana LEAP 1000

description: The Livelihood Empowerment against Poverty (LEAP) program provides cash and health insurance to extremely poor households in Ghana to encourage long-term development. It is the flagship program of Ghana’s National Protection Strategy and is overseen and implemented by the Department of Social Welfare (DSW) and the Ministry of Gender, Children, and Social Protection (MoGCSP). The initial trial phase for LEAP began in 2008, and then the program gradually expanded in 2009 and 2010. By early 2015 the program was active across Ghana and reached over 90,000 households. In 2015-2017 a pilot program called LEAP 1000 expanded eligibility criteria for the LEAP program, focusing on households with mothers and infants during the infants’ first 1000 days of life. It was aimed at improving infant nutrition and preventing stunting. The pilot focused on an initial 6,000 households in 10 districts that received bi-monthly cash transfers. This dataset covers the impact evaluation of this pilot phase. The data files within this dataset have been approved for public release by all partners, including the government of Ghana. They are restricted due to the potential for deductive disclosure. To access please fill out an application. Once an application is approved data will be available to download from the UNC Dataverse. Documentation includes data use instructions and the original questionnaires used during data collection.
DOI: 10.5281/zenodo.6502720
title: Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"

description: Model data and namelists for "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations" Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files). Info on variables is provided by the RAMS model variable guide (PDF, included). Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.
DOI: 10.7278/s50d-h9z0-5ft8
title: Dataset for: Howard Slough Waterfowl Management Area Multispectral Imagery at Various Resolutions and Convolutional Neural Network Training Data