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Research on electoral identifications and attitudes and voting behavior is among the most popular planes of analysis of the citizens' political participation. The presented article is a report from research carried out in the framework of the project "Political Preferences: Attitude – Identification – Behavior" in 2009 – 2014. It discusses the main assumptions and research directions, the tools used and, finally, the results obtained in the project. The presented research project is of a nationwide character, and is conducted on a representative sample of voters.

Data on primary ciliary dyskinesia (PCD) epidemiology is scarce and published studies are characterised by low numbers. In the framework of the European Union project BESTCILIA we aimed to combine all available datasets in a retrospective international PCD cohort (iPCD Cohort).

Data on primary ciliary dyskinesia (PCD) epidemiology is scarce and published studies are characterised by low numbers. In the framework of the European Union project BESTCILIA we aimed to combine all available datasets in a retrospective international PCD cohort (iPCD Cohort).We identified eligible datasets by performing a systematic review of published studies containing clinical information on PCD, and by contacting members of past and current European Respiratory Society Task Forces on PCD. We compared the contents of the datasets, clarified definitions and pooled them in a standardised format.As of April 2016 the iPCD Cohort includes data on 3013 patients from 18 countries. It includes data on diagnostic evaluations, symptoms, lung function, growth and treatments. Longitudinal data are currently available for 542 patients. The extent of clinical details per patient varies between centres. More than 50% of patients have a definite PCD diagnosis based on recent guidelines. Children aged 10-19 years are the largest age group, followed by younger children (≤9 years) and young adults (20-29 years).This is the largest observational PCD dataset available to date. It will allow us to answer pertinent questions on clinical phenotype, disease severity, prognosis and effect of treatments, and to investigate genotype-phenotype correlations.

Data on primary ciliary dyskinesia (PCD) epidemiology is scarce and published studies are characterised by low numbers. In the framework of the European Union project BESTCILIA we aimed to combine all available datasets in a retrospective international PCD cohort (iPCD Cohort). We identified eligible datasets by performing a systematic review of published studies containing clinical information on PCD, and by contacting members of past and current European Respiratory Society Task Forces on PCD. We compared the contents of the datasets, clarified definitions and pooled them in a standardised format. As of April 2016 the iPCD Cohort includes data on 3013 patients from 18 countries. It includes data on diagnostic evaluations, symptoms, lung function, growth and treatments. Longitudinal data are currently available for 542 patients. The extent of clinical details per patient varies between centres. More than 50% of patients have a definite PCD diagnosis based on recent guidelines. Children aged 10–19 years are the largest age group, followed by younger children (≤9 years) and young adults (20–29 years). This is the largest observational PCD dataset available to date. It will allow us to answer pertinent questions on clinical phenotype, disease severity, prognosis and effect of treatments, and to investigate genotype–phenotype correlations.

Marine environmental monitoring is undertaken to provide evidence that environmental management targets are being met. Moreover, monitoring also provides context to marine science and over the last century has allowed development of a critical scientific understanding of the marine environment and the impacts that humans are having on it. The seas around the UK are currently monitored by targeted, impact-driven, programmes (e.g., fishery or pollution based monitoring) often using traditional techniques, many of which have not changed significantly since the early 1900s. The advent of a new wave of automated technology, in combination with changing political and economic circumstances, means that there is currently a strong drive to move toward a more refined, efficient, and effective way of monitoring. We describe the policy and scientific rationale for monitoring our seas, alongside a comprehensive description of the types of equipment and methodology currently used and the technologies that are likely to be used in the future. We contextualize the way new technologies and methodologies may impact monitoring and discuss how whole ecosystems models can give an integrated, comprehensive approach to impact assessment. Furthermore, we discuss how an understanding of the value of each data point is crucial to assess the true costs and benefits to society of a marine monitoring programme.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 7 (2017): 40850, doi:10.1038/srep40850. ; The Arctic icescape is rapidly transforming from a thicker multiyear ice cover to a thinner and largely seasonal first-year ice cover with significant consequences for Arctic primary production. One critical challenge is to understand how productivity will change within the next decades. Recent studies have reported extensive phytoplankton blooms beneath ponded sea ice during summer, indicating that satellite-based Arctic annual primary production estimates may be significantly underestimated. Here we present a unique time-series of a phytoplankton spring bloom observed beneath snow-covered Arctic pack ice. The bloom, dominated by the haptophyte algae Phaeocystis pouchetii, caused near depletion of the surface nitrate inventory and a decline in dissolved inorganic carbon by 16 ± 6 g C m−2. Ocean circulation characteristics in the area indicated that the bloom developed in situ despite the snow-covered sea ice. Leads in the dynamic ice cover provided added sunlight necessary to initiate and sustain the bloom. Phytoplankton blooms beneath snow-covered ice might become more common and widespread in the future Arctic Ocean with frequent lead formation due to thinner and more dynamic sea ice despite projected increases in high-Arctic snowfall. This could alter productivity, marine food webs and carbon sequestration in the Arctic Ocean. ; This study was supported by the Centre for Ice, Climate and Ecosystems (ICE) at the Norwegian Polar Institute, the Ministry of Climate and Environment, Norway, the Research Council of Norway (projects Boom or Bust no. 244646, STASIS no. 221961, CORESAT no. 222681, CIRFA no. 237906 and AMOS CeO no. 223254), and the Ministry of Foreign Affairs, Norway (project ID Arctic), the ICE-ARC program of the European Union 7th Framework Program (grant number 603887), the ...

Special Feature: The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC).-- 42 pages, 16 figures, 3 tables, supplemental files https://doi.org/10.1525/elementa.2021.000046.-- Data accessibility statement: All data in this manuscript are publicly available from online repositories. Note that most data sets contain raw or preliminary data, while advanced versions will become available in future. The data may be found under the following references: drift track data (Figure 1, Nicolaus et al., doi:10.1594/PANGAEA.937204), observational dates (Figure 4, Nicolaus et al., doi:10.5281/zenodo.5898517), panorama photographs (Figure 5, Nicolaus et al., doi:10.1594/PANGAEA.938534), TLS data (Figure 6, Clemens-Sewall et al., doi:10.18739/A27S7HT3B), ROV radiation data (Figure 7, Nicolaus et al., doi:10.1594/PANGAEA.935688), surface albedo data on ground (Figure 8, Smith et al., broadband data under doi:10.18739/A2KK94D36 and spectral data under doi:10.18739/A2FT8DK8Z) and from the HELiX drone (Figure 8, Calmer et al., doi:10.18739/A2GH9BB0Q), on-ice RS data (Figure 10, Spreen et al., doi:10.5281/zenodo.5725870), surface images from thermal infrared and true color (Figure 11, Thielke et al, doi:10.1594/PANGAEA.934666), drift speed data from Polarstern (Figure 12, Nicolaus et al., doi:10.1594/PANGAEA.937204), deformation data from SAR (Figure 13, von Albedyll et al, doi:10.5281/zenodo.5195366), sea ice thickness and snow depth distribution (Figure 14, Hendricks et al., doi:10.5281/zenodo.5155244), sea ice physical properties (Figure 15, in Tables S2 and S3) with a sea ice core overview (Granskog et al., doi:10.5281/zenodo.4719905), snow pack properties (Figure 16, Macfarlane et al., doi:10.1594/PANGAEA.935934), and ship radar video sequence (Jäkel et al., doi:10.5446/52953) ; Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice ; This work was funded by the following: – the German Federal Ministry of Education and Research (BMBF) through financing the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung (AWI) and the Polarstern expedition PS122 under the grant N-2014-H-060_Dethloff, – the AWI through its projects: AWI_ROV, AWI_ICE, AWI_SNOW, AWI_ECO. The AWI buoy program and ROV work were funded by the Helmholtz strategic investment Frontiers in Arctic Marine Monitoring (FRAM), – the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Transregional Collaborative Research Centre TRR-172 "ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3" (grant 268020496), the International Research Training Group 1904 ArcTrain (grant 221211316), the MOSAiCmicrowaveRS project (grant 420499875), the HELiPOD grant (LA 2907/11-1), and the SCASI (NI 1096/5-1 and KA 2694/7-1) and SnowCast (AR1236/1) projects, – the BMBF through the projects Diatom-ARCTIC (03F0810A), IceSense (BMBF 03F0866A and 03F0866B), MOSAiC3-IceScan (BMBF 03F0916A), NiceLABpro (BMBF 03F0867A), SSIP (01LN1701A), and SIDFExplore (03F0868A), – the German Federal Ministry for Economic Affairs and Energy through the project ArcticSense (BMWi 50EE1917A), – the US National Science Foundation (NSF) through the project PROMIS (OPP-1724467, OPP-1724540, and OPP-1724748), the buoy work (OPP-1723400), the MiSNOW (OPP-1820927), the snow transect work (OPP-1820927), the sea ice coring work (OPP-1735862), the HELiX drone operations (OPP-1805569), surface energy fluxes (OPP-1724551), Climate Active Trace Gases (OPP-1807496), and Reactive Gas Chemistry (OPP-1914781). The last 4 of these were also supported by the NOAA Physical Sciences Laboratory, – the European Union's Horizon 2020 research and innovation program projects ARICE (grant 730965) for berth fees associated with the participation of the DEARice team and INTAROS (grant 727890) supporting the drone and albedo measurements, – the US Department of Energy Atmospheric Radiation Measurement (ARM) and Atmospheric System Research (ASR) programs (DE-SC0019251, DE-SC0021341), – the National Aeronautics and Space Administration (NASA) project 80NSSC20K0658, – the European Space Agency (ESA) MOSAiC microwave radiometer (EMIRAD2, ELBARA, HUTRAD), (EMIRAD2, ELBARA, HUTRAD), CIMRex (contract 4000125503/18/NL/FF/gp) and GNSS-R (P.O. 5001025474, C.N. 4000128320/19/NL/FF/ab) GNSS-R (contracts P.O. 5001025474 and C.N. 4000128320/19/NL/FF/ab) projects, – the Canadian Space Agency FAST project (grant no. 19FACALB08), – EUMETSAT support for microwave scatterometer measurements, – the Research Council of Norway through the projects HAVOC (grant no. 280292), SIDRiFT (grant no. 287871), and CAATEX (grant no. 280531), – the Fram Centre (Tromsø, Norway), from its flagship program on Arctic Ocean through the PHOTA project, – the UKRI Natural Environment Research Council (NERC) and BMBF, who jointly funded the Changing Arctic Ocean program (project Diatom Arctic, NE/R012849/1 and 03F0810A), – the UK Natural Environment Research Council (project SSAASI-CLIM grant NE/S00257X/1), – the Agencia Estatal de Investigación AEI of Spain (grant no. PCI2019-111844-2, RTI2018-099008-B-C22), – the Swedish Research Council (VR, grant no. 2018-03859), – the Swedish Polar Research Secretariat for berth fees for MOSAiC, – the Swiss Polar Institute project SnowMOSAiC, – the Werner-Petersen-Foundation for the development of a remotely operated floating platform (grant no. FKZ 2019/610). ; Peer reviewed

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Familienformen, Verwandtschaftsnetzwerke. Allgemeine Lebensumstände und Muster der gegenseitigen Unterstützung. Einkommen; Innerfamiliäre Transferleistungen. Praktische Unterstützung von staatlichen und offiziel anerkannten Versicherungen.

Themen: Erfassung von genealogischen Verbindungen von allen Verwandten durch Abstammung oder Heirat, darunter nicht mehr lebende Vorfahren und entfernte Verbindungen durch Abstammung oder Heirat. Für jedes Mitglied in diesem Netzwerk wurde erfragt: Geburtsort und derzeitiger Wohnort, wirtschaftliche Lage, Bildungsniveau, allgemeiner Gesundheitszustand, Indikator des Lebensstandards. Ähnliche Informationen über die Befragten selbst, einschließlich der eigenen wirtschaftlichen und gesundheitlichen Umstände, Informationen über die Häufigkeit und Art der sozialen Kontakte mit jedem Mitglied des Netzes der bekannten Verwandten (darunter rituelle Beziehungen wie Patenschaften).

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