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531 Research products, page 1 of 54

  • Rural Digital Europe
  • Research data
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  • Chinese
    Authors: 
    Junfang Niu; Junxia Feng; Xiying Zhang; Suying Chen; Liwei Shao;
    Publisher: Science Data Bank

    To understand how nocturnal warming (NW) affects the performance of maize (Zea mays L.), an open-field experiment with a free air temperature increase (FATI) facility was conducted for three seasons during 2014 to 2016 at Luancheng eco-agro-experimental station on the North China Plain (NCP). Three nocturnal warming scenarios were set up: the entire growing period (T1, from V4 to maturity), only the vegetative stages (T2, from V4 to a week presilking) and the reproductive stages (T3, from a week presilking to R6). The treatment without NW was the control.

  • Research data . 2022
    Open Access English
    Authors: 
    Langsrud, Solveig; Skuland, Silje E.;
    Publisher: Zenodo
    Project: EC | SafeConsumE (727580)

    The Risk-behaviour map is a document intended to aid access to and transfer of key data between research groups in the European project Safeconsume. The map covers only steps from retail to consumption for the case studies in Safeconsume where the consumer can reduce risk for foodborne infection (CCHs, Critical Consumer Handling). The map contains information about observed/reported behaviours that can affect risk for foodborne infection divided by country, consumer group, pathogen, food and step in the journey from retail to consumption. Details about data collection is given in: Skuland, S.E., Borda, D., Didier, P., Dumitras¸cu, L., Ferreira, V., Foden, M., Langsrud, S., Maître, I., Martens, L., Møretrø, T., Nguyen-The, C., Nicolau, A. I., Nunes, C., Rosenberg, T. G., Teigen, H. M., Teixeira, P., Truninger, M., 2020. European Food Safety: Mapping Critical Food Practices and Cultural Differences in France, Norway, Portugal, Romania and the UK, in: Skuland, S.E. (Ed.). SIFO report, Oslo. ODA Open Digital Archive: European food safety: Mapping critical food practices and cultural differences in France, Norway, Portugal, Romania and the UK (oslomet.no) Questions about the RM-map can be raised to the SafeConsume project coordinator: Solveig.langsrud@nofima.no Variable list: Name Description CCH/Critical steps Identification of the step and flow diagram the entry belongs to: The step in the flow diagram where the consumer through actions or choices can significantly reduce risk of foodborne infection The CCHs/critical steps belong to one of the following processes: Poultry and vegetables (PVF), Eggs (EGG), Shellfish (SHE), Ready-to-Eat (RTE). Each step is accompanied by the principle of risk reducing effect: Food choice: Buy or eat food with lower risk (e.g avoid buying food if not stored properly in shop, buying pasteurised products, choosing to eat food before use-by-date). Applies to all pathogens. Inhibit growth: Storing ready-to-eat food at cool temperature and consume within expiration date or adding preservatives. Applies to Listeria and Salmonella Wash/Remove: Wash vegetables and fruit. Applies to all pathogens Kill/Heat: Heat treatment to kill pathogens, freezing (Campylobacter) Personal hygiene: Avoid cross-contamination through hand washing or not touching food. Not preparing food when sick Hygiene: Avoid cross-contamination through washing surfaces and using clean utensils Cause or sources Description of causes and sources for the hazard to occur (presence, survival, transfer or growth of pathogen). See Appendix 3 for details Consumer Id Unique identifier of consumer. Pathogen The pathogen(s) that are relevant for the specific CCH/critical step Expert opinion: Effect on pathogen Effect of behaviour on the hazard estimated by a team of microbiologists. Effect on pathogen The effect on pathogen is an estimate of the change in the level of viable pathogens as a direct or indirect consequence of the behaviour, action or process. Consumer group, education, income, rural/urban and country When applicable, demographic data associated with the entry. Classification Name Attributes Classification, llist of codes/units CCH/Critical step Predefined, multiple choices EGG 1 Food choice EGG 3.2 Hygiene EGG 4a Inhibit growth EGG 4b Inhibit growth EGG 5.1 Hygiene EGG 5.2 Personal hygiene EGG 6a Kill EGG 6b Kill EGG 6c Food choice EGG 6c Inhibit growth EGG 7.3 Inhibit growth EGG 8.3 Inhibit growth EGG 9.1 Inhibit growth EGG 11.3 Inhibit growth PVF 1.1 Food choice PVF 1.2 Food choice PVF 2.1 Inhibit growth PVF 3a Inhibit growth PVF 3a Kill PVF 3b Inhibit growth PVF 5.1 Personal hygiene PVF 5.2 Personal hygiene PVF 6.1 Kill PVF 7a Wash/Remove PVF 7b Personal hygiene PVF 7b Wash/Remove PVF 8b Hygiene PVF 8b Personal Hygiene PVF 9.1 Hygiene PVF 10.1 Hygiene PVF 11.1 Inhibit growth PVF 11.2 Inhibit growth RTE 1.1 Food choice RTE 3.1 Inhibit growth RTE 6.1 Inhibit growth RTE 4b Personal hygiene RTE 5.2 Hygiene RTE 5.2 Personal hygiene RTE 6.1 Inhibit growth RTE 7.1 Inhibit growth RTE 7.2 Personal hygiene SHE 1.1 Food choice SHE 7.1 Kill No risk Not designated to CCH Causes/sources Free text Consumer ID Free text Pathogen Predefined, multiple choice Salmonella: S. Enterica Campylobacter: C. jejuni Listeria: Listeria monocytogenes Norovirus Toxoplasma: Toxoplasma gondii Expert opinion: Effect on pathogen Predefined High reduction: This behaviour will have a high reduction on the level of pathogens on food/surfaces/hands Median reduction: This behaviour will reduce the level of pathogens on food/surfaces/hands No effect: This behaviour will most likely not have a significant effect on the level of viable pathogens (< 1 log10 reduction/increase or less than 10 cells/particles transfer) For food choice: Random choice is rated as no effect Median increase: This behaviour will lead to a higher number of pathogens High increase: This behaviour will significantly increase the number of pathogens on food/surfaces/hands Effect on pathogen comment Free text Consumer group Predefined Elderly; >70 years, men and women Pregnant women; immunocompromized Young family: Couples (married or cohabitant) where the women is pregnant or living with their own child(ren) (including stepchildren and adopted children) aged less than 12 months Young, single man: Men age 20-29, Living alone or with flatmates Other: Consumers not belonging to the defined groups Educational level Predefined Basic Secondary Tertiary Not given Living area Predefined Rural Urban Income Predefined Low Median High Country Predefined Portugal, France, Romania, UK, Norway, Hungary, More details about the dataset can be achieved from the authors

  • Chinese
    Authors: 
    Wenyan Li; Ruibo Sun; Binbin Liu;
    Publisher: Science Data Bank

    Urease inhibitors are widely used in agricultural soils to reduce nitrogen loss, but their effects on soil microbial communities remain largely unknown. In a microcosm incubation experiment the urease inhibitor N-(n-butyl)thiophosphoric triamide (NBPT) was more effective than hydroquinone (HQ) in inhibiting urease in the studied soil, although both altered the soil prokaryotic community. The abundance of ureolytic microbes in the urea-supplemented soil was similar to that in the Control when urea was degraded. However, urease inhibitors, especially NBPT, enriched ureolysis groups. Thus, although urease inhibitors can temporarily suppress soil urease activity, they may result in a microbial community with enriched ureolytic groups, thereby increasing the difficulty of reducing nitrogen loss through ammonia volatilization.

  • Open Access
    Authors: 
    Krietemeyer, Andreas; Veldhuis, Marie-Claire ten; van de Giesen, N.C. (Nick);
    Publisher: 4TU.ResearchData
    Country: Netherlands
    Project: EC | BRIGAID (700699)

    RINEX3 (https://files.igs.org/pub/data/format/rinex305.pdf) Hatanaka-compressed (http://sopac.ucsd.edu/hatanaka.shtml) GNSS (Global Navigation Satellite System) data of single-frequency observations obtained by the Ublox Neo M8T receiver (Evaluation Toolkit) and standard Ublox patch antenna. The data is downsampled to 15-second observations spanning from DOY (day of year) 95 in 2017 to DOY 83 in 2020. The unit was placed on the rooftop of the observatory of the NMi building in Delft (approx. coordinate 51.986166, 4.387678). The receiver was connected to a constant power supply. The antenna was placed on the flat rooftop (no significant obstruction in the sky) with a 10cm circular glound plane underneath.

  • Open Access English
    Authors: 
    Nill, Leon; Grünberg, Inge; Ullmann, Tobias; Gessner, Matthias; Boike, Julia; Hostert, Patrick;
    Publisher: Zenodo

    Data to the publication by Nill et al. (2022) "Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic" The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada. We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details). Dataset information The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows: XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z XXXX-XXXX = epoch, e.g. 2015-2020 YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change) Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren) The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata: Datatype: Signed 16-bit integer (Int16) Data format: GeoTiff (.tif) No data value: -9999 Projection: EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]' Publication Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228 Further information For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de). A web-visualization of this dataset is available here.

  • Chinese
    Authors: 
    WANG Zifeng WANG Zifeng; LIU Junguo LIU Junguo;
    Publisher: Science Data Bank

    1) Data content (including elements and significance)This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin.2) Data sources and processing methodsIn this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused.3) Data quality descriptionValidations show that this data set has high spatial accuracy (Wang et al, 2021).4) Data application achievements and ProspectsThis data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.

  • Other research product . Other ORP type . 2022
    Closed Access Slovenian
    Authors: 
    Pezdevšek Malovrh, Špela;
    Publisher: Univerza v Ljubljani, Biotehniška fakulteta, Oddelek za gozdarstvo in obnovljive gozdne vire
    Country: Slovenia
  • Open Access
    Authors: 
    Melki, Paul; Bombrun, Lionel; Millet, Estelle; Diallo, Boubacar; ElChaoui ElGhor, Hakim; Da Costa, Jean-Pierre;
    Publisher: Zenodo

    These datasets accompany the article published in Remote Sensing entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing". For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: Raw Metrics: the raw evaluations for each image returned by each of the 12 evaluation metrics. Rankings: the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.

  • Other research product . Other ORP type . 2022
    Open Access Slovenian
    Authors: 
    Ficko, Andrej; Čotar, Maruša;
    Publisher: Biotehniška fakulteta, Oddelek za gozdarstvo in obnovljive gozdne vire
    Country: Slovenia
  • Open Access Slovenian
    Authors: 
    Ficko, Andrej;
    Publisher: Biotehniška fakulteta, Oddelek za gozdarstvo in obnovljive gozdne vire
    Country: Slovenia