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- Berlin, Martin, 1984-
(författare)
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Essays on the Determinants and Measurement of Subjective Well-Being
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis consists of four self-contained essays in economics, all concerned with different aspects of subjective well-being. The abstracts of the four studies are as follows.Beyond Income: The Importance for Life Satisfaction of Having Access to a Cash Margin. We study how life satisfaction among adult Swedes is influenced by having access to a cash margin, i.e. a moderate amount of money that could be acquired on short notice either through own savings, by loan from family or friends, or by other means. We find that cash margin is a strong and robust predictor of life satisfaction, also when controlling for individual fixed effects and socio-economic conditions, including income.Decomposing Variation in Daily Feelings: The Role of Time Use and Individual Characteristics. I explore the potential of using time-use data for understanding variation in affective well-being. Using the Princeton Affect and Time Survey, I decompose variation in daily affect into explained and unexplained within- and between person variation. Time use is found to mostly account for within-variation. Hence, its explanatory power is largely additive to that of individual characteristics. The explanatory power of time use is small, however. Activities only account for 1–7% of the total variation and this is not increased much by adding contextual variables.The Association Between Life Satisfaction and Affective Well-Being. We estimate the correlation between life satisfaction and affect — two conceptually distinct dimensions of subjective well-being. We propose a simple model that distinguishes between a stable and a transitory component of affect, and which also accounts for measurement error in self-reports of both variables, including current-mood bias effects on life satisfaction judgments. The model is estimated using momentarily measured well-being data, from an experience sampling survey that we conducted on a population sample of Swedes aged 18–50 (n=252). Our main estimates of the correlation between life satisfaction and long-run affective well-being range between 0.78 and 0.91, indicating a stronger convergence between these variables than many previous studies that do not account for measurement issues.Do OLS and Ordinal Happiness Regressions Yield Different Results? A Quantitative Assessment. Self-reported subjective well-being scores are often viewed as ordinal variables, but the conventional wisdom has it that OLS and ordered regression models (e.g. ordered probit) produce similar results when applied to such data. This claim has rarely been assessed formally, however, in particular with respect to quantifying the differences. I shed light on this issue by comparing the results from OLS and different ordered regression models, in terms of both statistical and economic significance, and across data sets with different response scales for measuring life satisfaction. The results are mixed. The differences between OLS, probit and logit estimates are typically small when the response scale has few categories, but larger, though not huge, when an 11-point scale is used. Moreover, when the error term is assumed to follow a skewed distribution, larger discrepancies are found throughout. I find a similar pattern in simulations, in which I assess how different methods perform with respect to the true parameters of interest, rather than to each other.
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| 2. |
- Johansson, Åsa, 1976-
(författare)
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Genome Variation in Human Populations : Exploring the Effects of Demographic History and the Potential for Mapping of Complex Traits
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A major challenge in human genetics is to understand the genetic variation underlying common diseases. In this thesis, I focus on forces creating differences between individuals and genomic regions, methods for characterizing genomic variation, and the association between genomic and phenotypic variation. Genetic markers are widely used to locate genes associated with different phenotypes. In my first paper, I describe novel algorithms for automatic genotype determination of microsatellite markers, a procedure which is currently both time-consuming and error prone. The co-segregation of genetic markers in a population leads to non-random association of alleles at different loci - linkage disequilibrium (LD). LD varies throughout the genome and differs between populations due to factors such as their demographic history. In my second paper, I discuss the increased power, for mapping of human traits, that results from studying a population with appreciable levels of LD such as is found in the Swedish Sami population. Lately, large-scale analyses of single nucleotide polymorphisms (SNPs) have become available and efforts have been made to identify a set of SNPs, which captures most of the genome variation in a population (tagSNPs). In my third paper, I describe the limitations of this approach when applied to data from an independent population sample of randomly ascertained SNPs. The transferability of tagSNPs between populations is poor, presumably due to variation in allele frequencies and the bias towards common SNPs used in most studies. The level of genomic variation is influenced by population structure, recombination and mutation rate, as well as natural selection. During the exodus from Africa, humans have adapted to new environmental conditions. In my fourth paper, I describe a new method for identifying genomic regions carrying signatures of recent positive selection and apply this to an available dataset of millions of SNPs.
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| 3. |
- Sheffield, Nathan C., et al.
(författare)
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From biomedical cloud platforms to microservices : next steps in FAIR data and analysis
- 2022
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Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 9:1
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The biomedical research community is investing heavily in biomedical cloud platforms. Cloud computing holds great promise for addressing challenges with big data and ensuring reproducibility in biology. However, despite their advantages, cloud platforms in and of themselves do not automatically support FAIRness. The global push to develop biomedical cloud platforms has led to new challenges, including platform lock-in, difficulty integrating across platforms, and duplicated effort for both users and developers. Here, we argue that these difficulties are systemic and emerge from incentives that encourage development effort on self-sufficient platforms and data repositories instead of interoperable microservices. We argue that many of these issues would be alleviated by prioritizing microservices and access to modular data in smaller chunks or summarized form. We propose that emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. We challenge funders, developers, and researchers to support a vision to improve interoperability through microservices as the next generation of cloud-based bioinformatics.
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