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1,756 Research products

  • SDSN - Greece
  • 2014-2023
  • Research data
  • Research software

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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: Okuljar, Magdalena; Garmash, Olga; Olin, Miska; Kalliokoski, Joni; +15 Authors

    This dataset supplements the following manuscript: Okuljar, M., Garmash, O., Olin, M., Kalliokoski, J., Timonen, H., Niemi, J. V., Paasonen, P., Kontkanen, J., Zhang, Y., Hellén, H., Kuuluvainen, H., Aurela, M., Manninen, H. E., Sipilä, M., Rönkkö, T., Petäjä, T., Kulmala, M., Dal Maso, M., and Ehn, M.: Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-524, 2023.

    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
    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 . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    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
      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 . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      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: Angelica Parisi; Francesca Avogadro di Valdengo; Ilaria Baneschi; Alice Baronetti; +9 Authors

    This dataset presents georeferenced measurements collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps. The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables, i.e. air and soil temperature, air relative humidity, soil volumetric water content, atmospheric pressure and solar irradiance. The measurements were conducted between 2017 and 2022 at five different sites at an elevation of approximately 2550-2750 meters a.s.l. To estimate NEE and ER, we employed the flux chamber method, measuring the temporal variation of carbon dioxide (CO2) concentration inside the chamber over a period of about 90 seconds. We used a customized portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyzer (IRGA) and a portable weather station. Measurements were taken at around 20 points per site during the snow-free season, spanning from June to October. The dataset is provided in a comma-separated text file (.csv) format. Each record corresponds to a single measurement point, with semicolons used as separators. The "NA" notation indicates values that are not available or have been excluded during quality control processes (e.g., due to battery failure). The sign convention for the fluxes is: a negative value indicates a CO2 flux from the atmosphere to the ecosystem, while a positive value represents a CO2 flux from the soil/ecosystem to the atmosphere. Consequently, ER values are positive, while NEE values can be positive or negative. The units for NEE and ER fluxes are molCO2 m-2 day-1 and μmolCO2 m-2 second-1. The first values in each record of the dataset indicate the observation details (sampling date, site, etc.), followed by the corresponding measured or calculated variables. NEE and ER values were estimated from the slope of the linear regression of CO2 concentration over time (ppm s-1) using a laboratory calibration curve. The calibration curve was created by relating known CO2 fluxes (within the range expected in the field) with the corresponding measured slopes. The flux values were then scaled up based on the area of the chamber base (0.036 m2) and adjusted using the ratio of atmospheric pressure and air temperature during the measurement to those recorded during the calibration in the laboratory.

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    ZENODO
    Dataset . 2023
    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 . 2023
      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: Poinhos, Rui; van der Lans, Ivo. A.; Rankin, Audrey; Fischer, Arnout R. H.; +8 Authors

    This data set consists of a large cross-country survey conducted for the EU funded project Food4Me in 2013. It contains determinants for consumer acceptance and intention to use of personalized nutrition advices based on different levels of specificity and personality of data –self-reported intake, blood analysis and DNA based analysis of optimal nutrition patterns. Data are provided in SPSS and interoperable .CSV format. Surveys and metainformation are added in interoperable .RTF format. The underlying survey is present in 8 languages English, German, Spanish, Norwegian, Dutch, Portuguese, Greek and Polish. Used scales are sourced to the originals in the description file.

    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 . 2023
    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; Research@WUR
    Dataset . 2023
    License: CC BY
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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
      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 . 2023
      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; Research@WUR
      Dataset . 2023
      License: CC BY
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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: Adrián González-Rosell; Imen Arfa; Maria Blanco;

    Data for the figures in the journal article: Gonzalez-Rosell A, Arfa I and Blanco M (2023) Introducing GoNEXUS SEF: a solutions evaluation framework for the joint governance of water, energy, and food resources. Sustainability Science. https://doi.org/10.1007/s11625-023-01324-1 Figure 3. Generation of new quantitative evidence: Percentage of variation of indicators between the water price scenario (WP) and baseline scenario (BS) for the year 2030. Source: Own elaboration based on the participatory SDM results (González-Rosell et al., 2020). Figure 4. Cross-impact matrix of the indicator system and solution from Table 2 for the water pricing in the Andalusia case study. Source: Own elaboration. Figure 5. Analysis of the degree of distribution of the network system based on the cross-impact matrix in Figure 4. Source: Own elaboration. Figure 6. (a) Full network graph: links between 16 indicators and WP solution based on the cross-impact matrix. (b) Tree network graph of the total influence of the WP solution at the first and second order based on the cross-impact matrix. Colour scale as in Figure 8. Synergies (green) and trade-off (red) of the WP solution on policy objectives and nexus objectives in Andalusia. Source: Own elaboration. {"references": ["Gonz\u00e1lez-Rosell A, Blanco M, and Arfa I (2020) Integrating Stakeholder Views and System Dynamics to Assess the Water\u2013Energy\u2013Food Nexus in Andalusia. Water, 12(11), 3172, https://doi.org/10.3390/w12113172"]}

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    ZENODO
    Dataset . 2023
    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 . 2023
    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
      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 . 2023
      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 . 2023
      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: Nino, Pasquale;

    Table S1 Field_data: dataset of winter wheat biophysical parameters (LAI, LCC and leaf N concentration), measured in the field Table S2 CI_CCC: Relationship between Chlorophyll indeces and Canopy Chlorophyll Content Table S3 NA_NC_NNI; results of calculations aimed at monitoring the evolution of the NNI, under different N management and meteorological condition.

    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 . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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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 . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Nascimento, Leonardo; Kuramochi, Takeshi; Iacobuta, Gabriela; den Elzen, Michel; +9 Authors

    Recommended Citation Citing this version NewClimate Institute, Wageningen University and Research & PBL Netherlands Environmental Assessment Agency. (2022). Climate Policy Database. DOI: 10.5281/zenodo.7774473 Citing all CPDB versions NewClimate Institute, Wageningen University and Research & PBL Netherlands Environmental Assessment Agency. (2016). Climate Policy Database. DOI: 10.5281/zenodo.7774109 Peer reviewed publication Nascimento, L. et al. (2022) 'Twenty years of climate policy: G20 coverage and gaps', Climate Policy, 22(2), pp. 158–174. DOI: 10.1080/14693062.2021.1993776. Description The Climate Policy Database (CDPB) is an open, collaborative tool to advance the data collection of the implementation status of climate policies. This project is funded by the European Union H2020 ELEVATE and ENGAGE projects and was, in its previous phase, funded under CD-Links. The database is maintained by NewClimate Institute with support from PBL Netherlands Environmental Assessment Agency and Wageningen University and Research. Although the CPDB exists since 2016, annual versions of the database have only been stored since 2019. Recommended Citation Citing this version NewClimate Institute, Wageningen University and Research & PBL Netherlands Environmental Assessment Agency. (2022). Climate Policy Database. DOI: 10.5281/zenodo.7774473 Citing all CPDB versions NewClimate Institute, Wageningen University and Research & PBL Netherlands Environmental Assessment Agency. (2016). Climate Policy Database. DOI: 10.5281/zenodo.7774109 Peer reviewed publication Nascimento, L. et al. (2022) 'Twenty years of climate policy: G20 coverage and gaps', Climate Policy, 22(2), pp. 158–174. DOI:10.1080/14693062.2021.1993776. The Climate Policy Database (CDPB) is an open, collaborative tool to advance the data collection of the implementation status of climate policies. This project is funded by the European Union H2020 ELEVATE and ENGAGE projects and was, in its previous phase, funded under CD-Links. The database is maintained by NewClimate Institute with support from PBL Netherlands Environmental Assessment Agency and Wageningen University and Research. Although the CPDB exists since 2016, annual versions of the database have only been stored since 2019.

    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 . 2023
    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 . 2023
    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 . 2023
    License: CC BY
    Data sources: ZENODO
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    Research@WUR
    Dataset . 2023
    Data sources: Research@WUR
    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 . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      Research@WUR
      Dataset . 2023
      Data sources: Research@WUR
      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 . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Rémi Vezy; Sebastian Munz; Noémie Gaudio; Marie Launay; +3 Authors

    This is the data, code and software to reproduce the article entitled " Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model". Here is a summary of the paper: The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems. In this study, we propose a set of simple and generic formalisms for simulating key interactions in intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Sunflower-Soybean, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for yield). This is the first time a complete set of formalisms has been developed and published for simulating intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing in silico the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.

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    ZENODO
    Dataset . 2023
    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 . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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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: Pancaldi, Francesco; Al Hassan, Mohamad; Schranz, Eric; Trindade, Luisa;

    This dataset contains 100 sets of 91 random QTL regions from the genome of Miscanthus sinensis that mirror the basepair size distribution of 91 QTLs that were found in Miscanthus sinensis through genome-wide association analysis (GWAS) for multiple traits related to biomass (cell wall) quality. The 100 sets of random QTL regions were used in the publication attached to this dataset to perform a permutation analysis to test for co-localization between a set of syntenic QTLs for cell wall quality previously mapped in Miscanthus and the QTLs detected by GWAS (see above). The 100 sets of random QTL regions have been produced in R, by applying the size distribution of GWAS QTLs as constraint for determining the size of random QTLs. Random QTLs could be picked across the whole miscanthus genome, to ensure complete randomness. 

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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    Authors: Pancaldi, Francesco;

    This dataset reports all the genetic polymorphisms (SNPs/INDELs) that were detected in all the genes included in the syntenic quantitative trait loci (SQTLs) for which intra-specific genomic diversity was studied by SQTLs alignment across genomes representing different plant accessions in the paper "Syntenic cell wall QTLs as versatile breeding tools: intra-specific allelic variability and predictability of biomass quality loci in target plant species" (currently under peer review; resource title and DOI will be added once the paper is published). The methodology followed for polymorphisms identification is detailly reported in the paper "Syntenic cell wall QTLs as versatile breeding tools: intra-specific allelic variability and predictability of biomass quality loci in target plant species". In brief, SQTL nucleotide sequences were aligned by NUCmer against diverse genomic assemblies from different accessions of six plant species (reported in the dataset). NUCmer outputs report SNPs and INDELs positions along SQTLs, whose data were used to infer changes in translated protein sequences in SQTL genes. Overall, the dataset contains 19 columns: - 1: SQTL ID - 2: The target chromosome over which SQTL produced alignment with NUCmer - 3: Gene ID for which SNPs/INDELs were reported (each row represents one gene from a SQTL) - 4-5: Process and function of a gene in the context of cell wall biosynthesis/biology (in the case a gene is a cell wall gene) - 6-7: The protein sequence and its length as coded by the gene sequence of the reference SQTLs - 8-9: The protein sequence and its length as coded by the gene sequence of the target chromosome against which SQTL produced alignment - 10-11: The number of SNPs and INDELs detected in the alignment of each gene - 12-15: Position and sequence effect of SNPs and INDELs in terms of stop codons (columns 12-13) and point/short amino acid changes (columns 14-15); - 16-19: General information on the assembly, species, and protein's first amino acid for each gene in alignment.

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    4TU.ResearchData
    Dataset . 2023
    License: CC BY
    Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2023
      License: CC BY
      Data sources: 4TU.ResearchData
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    Authors: Paladini de Mendoza, Francesco; Schroeder, Katrin; Langone, Leonardo; Chiggiato, Jacopo; +3 Authors

    This data set includes four files (CSV format) containing observational data from two oceanographic moorings, BB and FF, located in the Southern Adriatic Sea from the period between March 2012 and June 2020. The stand-alone moorings are equipped with a 300 kHz ADCP-RDI system, which measures currents along the last 100 meters of the water column and a CTD recorder equipped with SeaPoint turbidity meter sensor located approximatively 10 m above the bottom. The turbidity sensor measures in a range of 0-25 FTU. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic Sea submarine observatory system of the EMSO-ERIC European Consortium. The data were subjected to quality control (QC) and the coding numbers used, shown in a dedicated column, follow the SeaDataNet L20 measurement qualifiers flags. QC applied on echo data consists of detecting signal anomalies due to interactions with the seafloor and identifying if the signal falls below a minimum threshold for which the value is no longer considered reliable. For turbidity data, QC is addressed to the detections of possible spikes, anomalies, and sensor saturation in the recordings.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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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: Okuljar, Magdalena; Garmash, Olga; Olin, Miska; Kalliokoski, Joni; +15 Authors

    This dataset supplements the following manuscript: Okuljar, M., Garmash, O., Olin, M., Kalliokoski, J., Timonen, H., Niemi, J. V., Paasonen, P., Kontkanen, J., Zhang, Y., Hellén, H., Kuuluvainen, H., Aurela, M., Manninen, H. E., Sipilä, M., Rönkkö, T., Petäjä, T., Kulmala, M., Dal Maso, M., and Ehn, M.: Influence of anthropogenic emissions on the composition of highly oxygenated organic molecules in Helsinki: a street canyon and urban background station comparison, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-524, 2023.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
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    Authors: Angelica Parisi; Francesca Avogadro di Valdengo; Ilaria Baneschi; Alice Baronetti; +9 Authors

    This dataset presents georeferenced measurements collected at the Nivolet Plain in Gran Paradiso National Park (GPNP), western Italian Alps. The dataset includes the Net Ecosystem Exchange (NEE), Ecosystem Respiration (ER) and meteo-climatic variables, i.e. air and soil temperature, air relative humidity, soil volumetric water content, atmospheric pressure and solar irradiance. The measurements were conducted between 2017 and 2022 at five different sites at an elevation of approximately 2550-2750 meters a.s.l. To estimate NEE and ER, we employed the flux chamber method, measuring the temporal variation of carbon dioxide (CO2) concentration inside the chamber over a period of about 90 seconds. We used a customized portable non-steady-state dynamic flux chamber, paired with an InfraRed Gas Analyzer (IRGA) and a portable weather station. Measurements were taken at around 20 points per site during the snow-free season, spanning from June to October. The dataset is provided in a comma-separated text file (.csv) format. Each record corresponds to a single measurement point, with semicolons used as separators. The "NA" notation indicates values that are not available or have been excluded during quality control processes (e.g., due to battery failure). The sign convention for the fluxes is: a negative value indicates a CO2 flux from the atmosphere to the ecosystem, while a positive value represents a CO2 flux from the soil/ecosystem to the atmosphere. Consequently, ER values are positive, while NEE values can be positive or negative. The units for NEE and ER fluxes are molCO2 m-2 day-1 and μmolCO2 m-2 second-1. The first values in each record of the dataset indicate the observation details (sampling date, site, etc.), followed by the corresponding measured or calculated variables. NEE and ER values were estimated from the slope of the linear regression of CO2 concentration over time (ppm s-1) using a laboratory calibration curve. The calibration curve was created by relating known CO2 fluxes (within the range expected in the field) with the corresponding measured slopes. The flux values were then scaled up based on the area of the chamber base (0.036 m2) and adjusted using the ratio of atmospheric pressure and air temperature during the measurement to those recorded during the calibration in the laboratory.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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      ZENODO
      Dataset . 2023
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      Data sources: ZENODO
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    Authors: Poinhos, Rui; van der Lans, Ivo. A.; Rankin, Audrey; Fischer, Arnout R. H.; +8 Authors

    This data set consists of a large cross-country survey conducted for the EU funded project Food4Me in 2013. It contains determinants for consumer acceptance and intention to use of personalized nutrition advices based on different levels of specificity and personality of data –self-reported intake, blood analysis and DNA based analysis of optimal nutrition patterns. Data are provided in SPSS and interoperable .CSV format. Surveys and metainformation are added in interoperable .RTF format. The underlying survey is present in 8 languages English, German, Spanish, Norwegian, Dutch, Portuguese, Greek and Polish. Used scales are sourced to the originals in the description file.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
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    ZENODO; Research@WUR
    Dataset . 2023
    License: CC BY
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      ZENODO
      Dataset . 2023
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      Data sources: ZENODO
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      ZENODO; Research@WUR
      Dataset . 2023
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