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Forecast and evaluation datasets related to the urban air quality forecasting system urbanAQF. The forecast dataset contains the daily forecasts of year 2021 for the city of Hamburg in Germany. Forecast and evaluation data were used in the paper "Forecasting system for Urban Air Quality with automatic correction and web service for public dissemination" submitted to the InternationalJournal of Digital Earth.

[Methods] Data collection In order to identify phylogenetic and environmental correlates of plant mycorrhizal traits, we compiled four data sources: 1) plant species mycorrhizal trait data; 2) plant species occurrence data; 3) global climatic and soil environmental data and 4) plant phylogenetic information. The plant occurrence and environmental data were used to estimate plant species environmental associations. The environmental association data and phylogenetic information were then used to model variations in plant mycorrhizal trait expression (Figure 1). First, plant mycorrhizal trait data were obtained from the most up-to-date literature available, including data published by Harley & Harley (1987, 1990), Wang & Qiu (2006), Hempel et al. (2013), Bueno et al. (2017), Gerz et al. (2018) and Soudzilovskaia et al. (2020). We distinguished four plant mycorrhizal types (arbuscular (AM), ecto- (ECM), orchid (ORM), and ericoid (ERM) mycorrhiza, as defined by Smith & Read (2008)) and three plant mycorrhizal statuses (obligately (OM), facultatively (FM) and non-mycorrhizal (NM)). We compiled plant mycorrhizal trait data for 14,722 taxa at the species level (Appendix S2). Second, plant species occurrence records were retrieved from the Global Biodiversity Information Facility (GBIF; www.gbif.org) for 13,479 species that were present in both the standardised GBIF species list and the mycorrhizal trait species list. Third, plant occurrence data of 13,479 species were intersected with a 30 arc-seconds (approximately 1 km) global grid, with the presence or absence of each species in each cell recorded. A sensitivity test indicated that sampling 20 grid-cell records produced environmental parameter estimates that were representative of wider distribution areas (Appendix S1); retaining species recorded in ≥ 20 cells left 62,540,387 grid-cell level records for 11,770 species (Appendix S3). Environmental associations were approximated by intersecting the distribution data for each species with a raster stack of 54 environmental data layers (Table S1). Fourth, a phylogenetic tree containing the 11,770 species in our dataset was compiled, and phylogenetic signal in plant mycorrhizal traits was examined using the δ statistic (Borges et al., 2019). See Appendix S1 for details of datasets and data filtering. 710 dual mycorrhizal plant species (AM + ECM) were distinguished (Appendix S2), of which 665 were matched with geographic location information in GBIF. Except where stated otherwise, these species were grouped with ECM plant species in further analyses, reflecting ongoing controversy concerning the definition of dual mycorrhizal plant species (Teste et al., 2020; Brundrett, 2021a) and the fact that the niches of dual mycorrhizal plants most closely resemble those of ECM plants (Gerz et al., 2018). Mycorrhizal symbioses are known to strongly influence plant performance, structure plant communities and shape ecosystem dynamics. Plant mycorrhizal traits, such as those characterizing mycorrhizal type (arbuscular (AM), ecto-, ericoid, or orchid mycorrhiza) and status (obligately (OM), facultatively (FM), or non-mycorrhizal) offer valuable insight into plant belowground functionality. Here, we compile available plant mycorrhizal trait information and global occurrence data (~100 million records) for 11,770 vascular plant species. Using a plant phylogenetic mega-tree and high-resolution climatic and edaphic data layers, we assess phylogenetic and environmental correlates of plant mycorrhizal traits. We find that plant mycorrhizal type is more phylogenetically conserved than plant mycorrhizal status, while environmental variables (both climatic and edaphic; notably soil texture) explain more variation in mycorrhizal status, especially FM. The previously underestimated role of environmental conditions has far-reaching implications for our understanding of ecosystem functioning under changing climatic and soil conditions. Ramon y Cajal fellowship, Award: RYC2021-032533-I. European Research Council, Project ALFAwetlands. European Commission, Award: Centre of Excellence EcolChange. Alexander von Humboldt Foundation. Narodowa Agencja Wymiany Akademickiej, Award: PPN/ULM/2019/1/00248/U/00001. Estonian Research Council, Award: PRG1065.Estonian Research Council, Award: PRG1789.Estonian Research Council, Award: PRG741. Peer reviewed

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.

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.

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. 

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.

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.

Colletotrichum lupini, causing lupin anthracnose, is one of the worst pathogens to lupin cultivation worldwide. Understanding its population structure and evolutionary potential is crucial to design successful disease management strategies. The objective of this study was to employ population genetics to investigate the diversity, evolutionary dynamics and molecular basis of host interaction of this notorious lupin pathogen. A collection of globally representative C. lupini isolates was genotyped through triple digest restriction-site associated DNA sequencing (3D-RADseq), resulting in a dataset of unparalleled resolution. Phylogenetic and structural analysis could distinguish four (I – IV) independent lineages. The strong population structure, low recombination and slow linkage decay strongly suggests that C. lupini reproduces clonally. Different morphologies and virulence patterns on white (Lupinus albus) and Andean lupin (L. mutabilis) were observed between and within clonal lineages. Isolates belonging to lineage II were shown to have a mini-chromosome which was also partly present in lineage III and IV, but not in lineage I isolates. Variation in the presence of this mini-chromosome could indicate a role in host interaction. All four lineages were present in the South American Andes region, which is concluded to be the center of origin of this species. Only members of lineage II have been found outside South America since the 1990s, indicating it as the current pandemic population. As a seed-borne pathogen, C. lupini has mainly spread through infected but symptomless seeds, stressing the importance of phytosanitary measures to prevent future outbreaks of strains that are yet confined to South America.