Homogenizing GPS integrated water vapour time series: methodology and benchmarking the algorithms on synthetic datasets

We would like to thank the COST Action ES1206 GNSS4SWEC for financial support

Performance of various homogenization tools on a synthetic benchmark dataset of GPS and ERA-interim IWV differences

Presentación realizada en: IAG-IASPEI 39th Joint Scientific Assembly celebrada en Kobe, Japón, del 30 de julio al 4 de agosto de 2017

Study on homogenization of synthetic GNSS-Retrieved IWV time series and its impact on trend estimates with autoregressive noise

Póster presentado en: EGU General Assembly celebrada del 23 al 28 de abril de 2017 en Viena, Austria.A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of “Data homogenisation” of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less complicated and fully complicated. The synthetic dataset we present was used as a benchmark to test various statistical tools in terms of homogenisation task. In this research, we assess the impact of the noise model, trend and gaps on the performance of statistical methods to detect simulated change points

Multi-omics bioactivity profile-based chemical grouping and read-across:a case study with Daphnia magna and azo dyes

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this ‘omics approach are identified, the study illustrates an effective statistical approach for building chemical groups

Homogenization of tropospheric data: evaluating the algorithms under the presence of autoregressive process

Presentación realizada en: IX Hotine-Marussi Symposium celebrado en Roma del 18 al 22 de junio de 2018.This research was supported by the Polish National Science Centre, grant No. UMO-2016/21/B/ST10/02353

The Great American Crime Decline : Possible Explanations

This chapter examines the most important features of the crime decline in the United States during the 1990s-2010s but also takes a broader look at the violence declines of the last three centuries. The author argues that violent and property crime trends might have diverged in the 1990s, with property crimes increasingly happening in the online sphere and thus traditional property crime statistics not being reflective of the full picture. An important distinction is made between ‘contact crimes’ and crimes that do not require a victim and offender to be present in the same physical space. Contrary to the uncertainties engendered by property crime, the declines in violent (‘contact’) crime are rather general, and have been happening not only across all demographic and geographic categories within the United States but also throughout the developed world. An analysis of research literature on crime trends has identified twenty-four different explanations for the crime drop. Each one of them is briefly outlined and examined in terms of conceptual clarity and empirical support. Nine crime decline explanations are highlighted as the most promising ones. The majority of these promising explanations, being relative newcomers in the crime trends literature, have not been subjected to sufficient empirical scrutiny yet, and thus require further research. One potentially fruitful avenue for future studies is to examine the association of the most promising crime decline explanations with improvements in self-control

Data for "Within- and Between-Person Factor Structure of the Oldenburg Burnout Inventory: Analysis of a Diary Study using Multilevel Confirmatory Factor Analysis"

The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory, taking into account a hierarchical data structure: daily evaluations nested in persons. The participants were 235 employees of a public administration agency who assessed their burnout online immediately after office hours for 10 consecutive working days (from Monday to Friday). Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure tested at the within- and between-person levels. The results were ambiguous, as both models showed a reasonable fit to the data. However, since exhaustion and disengagement were strongly correlated at each level and within-person reliability for disengagement was rather low, a unidimensional model seems more valid. Nonetheless, cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. These results suggest that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout experiences that influence person-level scores on the items. Ignoring these across-level discrepancies may lead to biased conclusions, since individuals were found to be an unexpected source of construct variability in our stud

Data for "Within- and Between-Person Factor Structure of the Oldenburg Burnout Inventory: Analysis of a Diary Study using Multilevel Confirmatory Factor Analysis"

The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory, taking into account a hierarchical data structure: daily evaluations nested in persons. The participants were 235 employees of a public administration agency who assessed their burnout online immediately after office hours for 10 consecutive working days (from Monday to Friday). Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure tested at the within- and between-person levels. The results were ambiguous, as both models showed a reasonable fit to the data. However, since exhaustion and disengagement were strongly correlated at each level and within-person reliability for disengagement was rather low, a unidimensional model seems more valid. Nonetheless, cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. These results suggest that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout experiences that influence person-level scores on the items. Ignoring these across-level discrepancies may lead to biased conclusions, since individuals were found to be an unexpected source of construct variability in our stud

Data for "Within- and Between-Person Factor Structure of the Oldenburg Burnout Inventory: Analysis of a Diary Study using Multilevel Confirmatory Factor Analysis"

The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory, taking into account a hierarchical data structure: daily evaluations nested in persons. The participants were 235 employees of a public administration agency who assessed their burnout online immediately after office hours for 10 consecutive working days (from Monday to Friday). Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure tested at the within- and between-person levels. The results were ambiguous, as both models showed a reasonable fit to the data. However, since exhaustion and disengagement were strongly correlated at each level and within-person reliability for disengagement was rather low, a unidimensional model seems more valid. Nonetheless, cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. These results suggest that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout experiences that influence person-level scores on the items. Ignoring these across-level discrepancies may lead to biased conclusions, since individuals were found to be an unexpected source of construct variability in our stud