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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dietze, Elisabeth; Karger, Cornelia; Mangelsdorf, Kai;

    We freeze-dried and homogenized 44 samples of c. 0.7-1.8 g dry sediment from core PG1351 covering late glacials and interglacials of MIS 8 to MIS 5e, integrating sediment of 1 cm core depth. Temporal resolution of these samples ranges from 140 to 960 years per sample. For the period between 430 and 405 kyrs ago (end of MIS 12 to MIS 11c), 13 samples of 0.5-1.3 g of dry sediment from ICDP core 5011-1 were taken for MA analyses, integrating sediment of 2 cm core depth. Eight of these 13 samples are from the same core depths as were previously analysed for pollen (Melles et al., 2012). Temporal resolution of these samples varies between 200 and 970 years per sample comparable to core PG1351. Across all samples, temporal resolution is 333 ± 273 years per sample, giving centennial- to millennial scale averages. We extracted the polar lipids of all MA samples using a Dionex Accelerated Solvent Extraction system (ASE 350, ThermoFisher Scientific) at 100°C, 103 bar pressure and two extraction cycles (20 min static time) with 100 % methanol, after an ASE cycle with 100 % dichloromethane. For every sample sequence (n=13-18), we extracted a blank ASE cell and included it in all further steps. We added 60 ng of deuterated levoglucosan (C6H3D7O5; dLVG; Th. Geyer GmbH & Co. KG) as internal standard, and filtered the extract over a PTFE filter using acetonitrile and 5 % HPLC-grade water. We analysed the extracts with an Ultimate 3000 RS ultra-high performance liquid chromatograph (U-HPLC) with thermostated autosampler and column oven coupled to a Q Exactive Plus Orbitrap mass spectrometer (Quadrupole-Orbitrap MS; ThermoFisher Scientific) with heated electrospray injection (HESI) probe at GFZ Potsdam, using measurement conditions adapted from earlier studies (Hopmans et al., 2013;Schreuder et al., 2018;Dietze et al., 2019). Briefly, separation was achieved on two Xbridge BEH amide columns in series (2.1 x 150 mm, 3.5 um particle size) fitted with a 50 mm pre-column of the same material (Waters). The compounds were eluted (flow rate 0.2 mL min-1) with 100 % A for 15 minutes, followed by column cleaning with 100 % B for 15 min, and re-equilibration to starting conditions for 25 min. Eluent A was acetonitrile:water:triethylamine (92.5:7.5:0.01) and eluent B acetonitrile:water:triethylamine (70:30:0.01). HESI settings were as follows: sheath gas (N2) pressure 20 (arbitrary units), auxiliary gas (N2) pressure 3 (arbitrary units), auxiliary gas (N2) temperature of 50 ˚C, spray voltage -2.9 kV (negative ion mode), capillary temperature 300 °C, S-Lens 50 V. Detection was achieved by monitoring m/z 150-200 with a resolution of 280,000 ppm. Targeted data dependent MS2 (normalized collision energy 13 V) was performed on any signal within 10 ppm of m/z 161.0445 (calculated exact mass of deprotonated levoglucosan and its isomers) or m/z 168.0884 (calculated exact mass of deprotonated dLVG) with an isolation window of 0.4 m/z. The detection limit was 2.5 pg on column, based on injections of 0.5 to 5000 pg on column of authentic standards of LVG, MAN, and GAL (Santa Cruz Biotechnology) and dLVG. Integrations were performed on mass chromatograms within 3 ppm mass accuracy and corrected for relative response factors to dLVG (1.08 ± 0.10, 0.76 ± 0.10 and 0.24 ± 0.05 for LVG, MAN, and GAL, respectively), according to known authentic standard mixes injected before and after every measurement sequence and supported by characteristic isomer-specific MS² data. All samples were corrected by subtracting the maximum MA concentrations in the blank duplicates of each ASE sequence. To account for biases due to sediment properties and sedimentation rates, MA influxes (mass accumulation rates in ng cm-2 yr-1) were calculated by multiplying the concentrations (ng g-1) with the sample-specific dry bulk densities (Melles et al., 2007;Wennrich et al., 2016), and the sample's sedimentation rates (cm yr-1) using the age-depth models presented by Nowaczyk et al. (2013) for the the PG1351 and the ICDP-5011-1 cores.

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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PANGAEA - Data Publi...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      PANGAEA
      Dataset . 2020
      Data sources: B2FIND
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  • Authors: Stinglhamer, Martin; Yzeiri, Xheila; Rohlfs, Tabea; Brandhofer, Tobias; +2 Authors

    An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: Martin Stinglhamer, Xheila Yzeiri, Tabea Rohlfs, Tobias Brandhofer, Constantin G. Daniliuc, Olga García Mancheño|2022|ACS Org. Inorg. Au|2|496|doi:10.1021/acsorginorgau.2c00026

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    Authors: Lefrancq, Coline; Hawkes, Jason D.;

    This table contains the quantities of each pottery class at each site identified during archaeological surveys in Vidarbha, Maharashtra, India. The data is arranged according to the archaeological sites. For each site, the number of sherds and the MNI count are presented.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bjørn Peare Bartholdy; Shira Gur-Arieh;

    FTIR analysis data on a calcifying oral biofilm model. Includes metadata, raw data and images of spectra. README.md file included in ftir_raw-data.zip.FTIR analysis: A few ug of each sample were repeatedly ground together with KBr and pressed in a 7 mm die under two tons of pressure using a Specac mini-pellet press. Samples were analysed at the Laboratory for Sedimentary Archaeology, Haifa University. The analysis was conducted with a Thermo Nicolet is5 spectrometer intransmission, at 4 cm<sup>-1</sup> resolution, with an average of 32 scans between wavenumbers 4000 and 400 cm<sup>-1</sup>.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCIS; 4TU.Research...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2023
    License: CC 0
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2023
    License: CC 0
    Data sources: 4TU.ResearchData
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ NARCIS; 4TU.Research...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      4TU.ResearchData
      Dataset . 2023
      License: CC 0
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2023
      License: CC 0
      Data sources: 4TU.ResearchData
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wciślik, Piotr; Maryl, Maciej; Vienni Baptista, Bianca; Schriber, Lucien;

    Background and methodology: The subject co-occurrence matrix represents the pairs of All Science Journal Classification (ASJC) disciplines that co-occur in journals represented in the SHAPE-ID Literature Review dataset, prepared for the purposes of quantitative analysis. We take disciplinary affiliations of journals as a proxy of disciplinary characteristics of the journal articles in the Literature Review dataset, mindful of the fact that a particular article might deviate from the disciplinary affiliation of the journal in which it was published. However, since there was no data readily available on item level, and manual disciplinary encoding of all the items in the bibliography was beyond the scope of this study, the method used is the best approximation of the presence of discourse on interdisciplinarity and transdisciplinarity, in and between disciplines. In the matrix, each co-occurrence value is weighted by the number of journals that feature the given pair of disciplines, and by the number of articles represented in the dataset that feature in these journals. E.g. if Journals J1 and J2 each featured disciplines D1 and D2, and if 4 articles from J1 and 7 articles from J2 are represented in the SHAPE-ID Literature Review dataset, the co-occurrence value is 11. The pairings cross-referencing a single discipline (e.g. 1202 History in both first row and first column) correspond to the co-occurence value of mono-disciplinary journals. Description of the file: This is a csv file containing a 308x308 cell matrix with ASJC disciplines in first rows and columns, and co-occurrence value in the remaining cells.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2020
    License: CC BY
    Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2020
      License: CC BY
      Data sources: ZENODO; Sygma
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    Authors: García-Diego, Fernando-Juan; Zarzo, Manuel; Ramírez, Sandra; Perles, Angel;

    This data set contains relative humidity measurements obtained with sensors installed in the apse vault of the Cathedral of Valencia in Spain. The interest of these sensors is to monitor the conservation conditions of Renaissance frescoes. Included files are: Cathedral_of_Valencia_RH_2008.csv : Relative humidity measurements for the year 2008 Cathedral_of_Valencia_RH_2010.csv : Relative humidity measurements for the year 2010

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    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2021
    License: CC BY
    Data sources: Datacite; Sygma
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      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2021
      License: CC BY
      Data sources: Datacite; Sygma
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  • Authors: Lercari, Nicola; Jaffke, Denise; McAvoy, Scott; Campiani, Arianna; +3 Authors

    The IBM 3D models and derivative products were generated via a typical Image-based modeling pipeline. The aerial photos were collected by the UC Merced team using a DJI Phantom 3 Pro multi-rotor drone. The geospatial control data was collected by California State Parks licensed surveyor David Gutierrez using a Trimble differential GPS and Trimble robotic total station. The TLS LiDAR data were collected by the UC Merced team using a FARO Focus 3D S120 range finder and processed in FARO Scene and CloudCompare. The CAD drawings were created by Arianna Campiani using orthographic views of the buildings' terrestrial laser scanning point clouds produced by Scott McAvoy in the Potree Viewer. Dataset includes all the natively digital geospatial and 3D data collected in our topographic and 3D survey of the iconic Dechambeau Hotel & IOOF Hall buildings located at the entrance of town on Main St.

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    Authors: Burgay, Francois; Spolaor, Andrea; Gabrieli, Jacopo; Cozzi, Giulio; +3 Authors

    In this dataset, we present Fe, Na and Ca concentration and fluxes retrieved from the North Greenland Eemian Ice Drilling (NEEM) ice core project, covering the last 108 kyrs. The sampling resolution was 110 cm. To ensure an effective dissolution of Fe particles, samples were acidified to pH 1 using Suprapure nitric acid and stored at room temperature for 1 month before the analysis. The ice samples were analyzed with an Inductively Coupled Plasma Single Quadrupole Mass Spectrometer equipped with a quartz Scott spray chamber. Limits of Detection, calculated as three times the standard deviation of the blanks, were 0.8 µg L-1 for 57Fe, 1 µg L-1 for Ca and 3 µg L-1 for Na. Our results show that Holocene Fe fluxes (0.042 -11.7 kyr b2k, 0.5 mg m-2 yr-1) at the NEEM site were four times lower than the average recorded over the last glacial period (11.7– 108 kyr b2k, 2.0 mg m-2 yr-1), while they were greater during the Last Glacial Maximum (LGM, 14.5 – 26.5 kyr b2k, 3.6 mg m-2 yr-1) and Marine Isotope Stage 4 (MIS 4, 60 - 71 kyr b2k, 5.8 mg m-2 yr-1). We present Fe, Ca and Na concentration and fluxes. Dating (GICC05modelext-NEEM-1) and accumulation data are from Rasmussen et al., 2013.

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    PANGAEA
    Dataset . 2021
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      PANGAEA
      Dataset . 2021
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    Authors: Gómez-Letona, Markel; Baumann, Moritz; González, Acorayda; Pérez Barrancos, Clàudia; +5 Authors

    This dataset contains the dissolved organic matter (DOM) quantification and optical characterisation results from a KOSMOS mesocosm experiment carried out in the framework of the Ocean Artificial Upwelling project. The experiment was carried out in the autumn of 2018 in the oligotrophic waters of Gran Canaria. During the 39 days of experiment nutrient-rich deep water was added to the mesocosms in two modes (singular vs recurring additions), with four levels of intensity. Dissolved organic carbon, nitrogen and phosphorus were quantified with a Shimadzu TOC-5000 and a QuAAtro AutoAnalyzer. The absorption and fluorescence proprieties of DOM were determined making use of an Ocean Optics USB2000+UV-VIS-ES Spectrometer and a Jobin Yvon Horiba Fluoromax-4 spectrofluorometer, respectively. The aim of this dataset was to study the effect of artificial upwelling on the dissolved organic matter pool and its potential implications for carbon sequestration.

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  • A Data Guide for this study is available as a web page and for download. The National Longitudinal Study of Adolescent to Adult Health (Add Health), 1994-2008 [Public Use] is a longitudinal study of a nationally representative sample of U.S. adolescents in grades 7 through 12 during the 1994-1995 school year. The Add Health cohort was followed into young adulthood with four in-home interviews, the most recent conducted in 2008 when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents' social, economic, psychological, and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships. Add Health Wave I data collection took place between September 1994 and December 1995, and included both an in-school questionnaire and in-home interview. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12, and gathered information on social and demographic characteristics of adolescent respondents, education and occupation of parents, household structure, expectations for the future, self-esteem, health status, risk behaviors, friendships, and school-year extracurricular activities. All students listed on a sample school's roster were eligible for selection into the core in-home interview sample. In-home interviews included topics such as health status, health-facility utilization, nutrition, peer networks, decision-making processes, family composition and dynamics, educational aspirations and expectations, employment experience, romantic and sexual partnerships, substance use, and criminal activities. A parent, preferably the resident mother, of each adolescent respondent interviewed in Wave I was also asked to complete an interviewer-assisted questionnaire covering topics such as inheritable health conditions, marriages and marriage-like relationships, neighborhood characteristics, involvement in volunteer, civic, and school activities, health-affecting behaviors, education and employment, household income and economic assistance, parent-adolescent communication and interaction, parent's familiarity with the adolescent's friends and friends' parents. Add Health data collection recommenced for Wave II from April to August 1996, and included almost 15,000 follow-up in-home interviews with adolescents from Wave I. Interview questions were generally similar to Wave I, but also included questions about sun exposure and more detailed nutrition questions. Respondents were asked to report their height and weight during the course of the interview, and were also weighed and measured by the interviewer. From August 2001 to April 2002, Wave III data were collected through in-home interviews with 15,170 Wave I respondents (now 18 to 26 years old), as well as interviews with their partners. Respondents were administered survey questions designed to obtain information about family, relationships, sexual experiences, childbearing, and educational histories, labor force involvement, civic participation, religion and spirituality, mental health, health insurance, illness, delinquency and violence, gambling, substance abuse, and involvement with the criminal justice system. High School Transcript Release Forms were also collected at Wave III, and these data comprise the Education Data component of the Add Health study. Wave IV in-home interviews were conducted in 2008 and 2009 when the original Wave I respondents were 24 to 32 years old. Longitudinal survey data were collected on the social, economic, psychological, and health circumstances of respondents, as well as longitudinal geographic data. Survey questions were expanded on educational transitions, economic status and financial resources and strains, sleep patterns and sleep quality, eating habits and nutrition, illnesses and medications, physical activities, emotional content and quality of current or most recent romantic/cohabiting/marriage relationships, and maltreatment during childhood by caregivers. Dates and circumstances of key life events occurring in young adulthood were also recorded, including a complete marriage and cohabitation history, full pregnancy and fertility histories from both men and women, an educational history of dates of degrees and school attendance, contact with the criminal justice system, military service, and various employment events, including the date of first and current jobs, with respective information on occupation, industry, wages, hours, and benefits. Finally, physical measurements and biospecimens were also collected at Wave IV, and included anthropometric measures of weight, height and waist circumference, cardiovascular measures such as systolic blood pressure, diastolic blood pressure, and pulse, metabolic measures from dried blood spots assayed for lipids, glucose, and glycosylated hemoglobin (HbA1c), measures of inflammation and immune function, including High sensitivity C-reactive protein (hsCRP) and Epstein-Barr virus (EBV). Datasets: DS0: Study-Level Files DS1: Wave I: In-Home Questionnaire, Public Use Sample DS2: Wave I: Public Use Contextual Database DS3: Wave I: Network Variables DS4: Wave I: Public Use Grand Sample Weights DS5: Wave II: In-Home Questionnaire, Public Use Sample DS6: Wave II: Public Use Contextual Database DS7: Wave II: Public Use Grand Sample Weights DS8: Wave III: In-Home Questionnaire, Public Use Sample DS9: Wave III: In-Home Questionnaire, Public Use Sample (Section 17: Relationships) DS10: Wave III: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancies) DS11: Wave III: In-Home Questionnaire, Public Use Sample (Section 19: Relationships in Detail) DS12: Wave III: In-Home Questionnaire, Public Use Sample (Section 22: Completed Pregnancies) DS13: Wave III: In-Home Questionnaire, Public Use Sample (Section 23: Current Pregnancies) DS14: Wave III: In-Home Questionnaire, Public Use Sample (Section 24: Live Births) DS15: Wave III: In-Home Questionnaire, Public Use Sample (Section 25: Children and Parenting) DS16: Wave III: Public Use Education Data DS17: Wave III: Public Use Graduation Data DS18: Wave III: Public Use Education Data Weights DS19: Wave III: Add Health School Weights DS20: Wave III: Peabody Picture Vocabulary Test (PVT), Public Use DS21: Wave III: Public In-Home Weights DS22: Wave IV: In-Home Questionnaire, Public Use Sample DS23: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16B: Relationships) DS24: Wave IV: In-Home Questionnaire, Public Use Sample (Section 16C: Relationships) DS25: Wave IV: In-Home Questionnaire, Public Use Sample (Section 18: Pregnancy Table) DS26: Wave IV: In-Home Questionnaire, Public Use Sample (Section 19: Live Births) DS27: Wave IV: In-Home Questionnaire, Public Use Sample (Section 20A: Children and Parenting) DS28: Wave IV: Biomarkers, Measures of Inflammation and Immune Function DS29: Wave IV: Biomarkers, Measures of Glucose Homeostasis DS30: Wave IV: Biomarkers, Lipids DS31: Wave IV: Public Use Weights Wave I: The Stage 1 in-school sample was a stratified, random sample of all high schools in the United States. A school was eligible for the sample if it included an 11th grade and had a minimum enrollment of 30 students. A feeder school -- a school that sent graduates to the high school and that included a 7th grade -- was also recruited from the community. The in-school questionnaire was administered to more than 90,000 students in grades 7 through 12. The Stage 2 in-home sample of 27,000 adolescents consisted of a core sample from each community, plus selected special over samples. Eligibility for over samples was determined by an adolescent's responses on the in-school questionnaire. Adolescents could qualify for more than one sample.; Wave II: The Wave II in-home interview surveyed almost 15,000 of the same students one year after Wave I.; Wave III: The in-home Wave III sample consists of over 15,000 Wave I respondents who could be located and re-interviewed six years later.; Wave IV: All original Wave I in-home respondents were eligible for in-home interviews at Wave IV. At Wave IV, the Add Health sample was dispersed across the nation with respondents living in all 50 states. Administrators were able to locate 92.5% of the Wave IV sample and interviewed 80.3% of eligible sample members. ; For additional information on sampling, including detailed information on special oversamples, please see the Add Health Study Design page. Add Health was developed in response to a mandate from the U.S. Congress to fund a study of adolescent health. Waves I and II focused on the forces that may influence adolescents' health and risk behaviors, including personal traits, families, friendships, romantic relationships, peer groups, schools, neighborhoods, and communities. As participants aged into adulthood, the scientific goals of the study expanded and evolved. Wave III explored adolescent experiences and behaviors related to decisions, behavior, and health outcomes in the transition to adulthood. Wave IV expanded to examine developmental and health trajectories across the life course of adolescence into young adulthood, using an integrative study design which combined social, behavioral, and biomedical measures data collection. Response Rates: Response rates for each wave were as follows: Wave I: 79 percent; Wave II: 88.6 percent; Wave III: 77.4 percent; Wave IV: 80.3 percent; Adolescents in grades 7 through 12 during the 1994-1995 school year. Respondents were geographically located in the United States. audio computer-assisted self interview (ACASI) computer-assisted personal interview (CAPI) computer-assisted self interview (CASI) paper and pencil interview (PAPI) face-to-face interview

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    Authors: Dietze, Elisabeth; Karger, Cornelia; Mangelsdorf, Kai;

    We freeze-dried and homogenized 44 samples of c. 0.7-1.8 g dry sediment from core PG1351 covering late glacials and interglacials of MIS 8 to MIS 5e, integrating sediment of 1 cm core depth. Temporal resolution of these samples ranges from 140 to 960 years per sample. For the period between 430 and 405 kyrs ago (end of MIS 12 to MIS 11c), 13 samples of 0.5-1.3 g of dry sediment from ICDP core 5011-1 were taken for MA analyses, integrating sediment of 2 cm core depth. Eight of these 13 samples are from the same core depths as were previously analysed for pollen (Melles et al., 2012). Temporal resolution of these samples varies between 200 and 970 years per sample comparable to core PG1351. Across all samples, temporal resolution is 333 ± 273 years per sample, giving centennial- to millennial scale averages. We extracted the polar lipids of all MA samples using a Dionex Accelerated Solvent Extraction system (ASE 350, ThermoFisher Scientific) at 100°C, 103 bar pressure and two extraction cycles (20 min static time) with 100 % methanol, after an ASE cycle with 100 % dichloromethane. For every sample sequence (n=13-18), we extracted a blank ASE cell and included it in all further steps. We added 60 ng of deuterated levoglucosan (C6H3D7O5; dLVG; Th. Geyer GmbH & Co. KG) as internal standard, and filtered the extract over a PTFE filter using acetonitrile and 5 % HPLC-grade water. We analysed the extracts with an Ultimate 3000 RS ultra-high performance liquid chromatograph (U-HPLC) with thermostated autosampler and column oven coupled to a Q Exactive Plus Orbitrap mass spectrometer (Quadrupole-Orbitrap MS; ThermoFisher Scientific) with heated electrospray injection (HESI) probe at GFZ Potsdam, using measurement conditions adapted from earlier studies (Hopmans et al., 2013;Schreuder et al., 2018;Dietze et al., 2019). Briefly, separation was achieved on two Xbridge BEH amide columns in series (2.1 x 150 mm, 3.5 um particle size) fitted with a 50 mm pre-column of the same material (Waters). The compounds were eluted (flow rate 0.2 mL min-1) with 100 % A for 15 minutes, followed by column cleaning with 100 % B for 15 min, and re-equilibration to starting conditions for 25 min. Eluent A was acetonitrile:water:triethylamine (92.5:7.5:0.01) and eluent B acetonitrile:water:triethylamine (70:30:0.01). HESI settings were as follows: sheath gas (N2) pressure 20 (arbitrary units), auxiliary gas (N2) pressure 3 (arbitrary units), auxiliary gas (N2) temperature of 50 ˚C, spray voltage -2.9 kV (negative ion mode), capillary temperature 300 °C, S-Lens 50 V. Detection was achieved by monitoring m/z 150-200 with a resolution of 280,000 ppm. Targeted data dependent MS2 (normalized collision energy 13 V) was performed on any signal within 10 ppm of m/z 161.0445 (calculated exact mass of deprotonated levoglucosan and its isomers) or m/z 168.0884 (calculated exact mass of deprotonated dLVG) with an isolation window of 0.4 m/z. The detection limit was 2.5 pg on column, based on injections of 0.5 to 5000 pg on column of authentic standards of LVG, MAN, and GAL (Santa Cruz Biotechnology) and dLVG. Integrations were performed on mass chromatograms within 3 ppm mass accuracy and corrected for relative response factors to dLVG (1.08 ± 0.10, 0.76 ± 0.10 and 0.24 ± 0.05 for LVG, MAN, and GAL, respectively), according to known authentic standard mixes injected before and after every measurement sequence and supported by characteristic isomer-specific MS² data. All samples were corrected by subtracting the maximum MA concentrations in the blank duplicates of each ASE sequence. To account for biases due to sediment properties and sedimentation rates, MA influxes (mass accumulation rates in ng cm-2 yr-1) were calculated by multiplying the concentrations (ng g-1) with the sample-specific dry bulk densities (Melles et al., 2007;Wennrich et al., 2016), and the sample's sedimentation rates (cm yr-1) using the age-depth models presented by Nowaczyk et al. (2013) for the the PG1351 and the ICDP-5011-1 cores.

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    PANGAEA
    Dataset . 2020
    Data sources: B2FIND
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      Dataset . 2020
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  • Authors: Stinglhamer, Martin; Yzeiri, Xheila; Rohlfs, Tabea; Brandhofer, Tobias; +2 Authors

    An entry from the Cambridge Structural