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15,430 Research products, page 1 of 1,543

  • Rural Digital Europe
  • Research data
  • Research software
  • 2018-2022

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  • Open Access English
    Authors: 
    de Almeida, Cátia Rodrigues; Santos, Douglas; Vasques, Julia Tucker; Cardoso-Fernandes, Joana; Lima, Alexandre; Teodoro, Ana C.;
    Publisher: Zenodo

    Studies focused on methodologies for locating and prospecting Li-Cs-Ta (LCT) pegmatites are increasingly relevant, given their importance for the energy market in a scenario where new sources need to be identified. Considering the inherent costs of field campaigns to identify targets in situ, this study presents alternatives, focusing on a preliminary evaluation of the spectral signature of targets at a specific site to serve as an added value for future exploration studies. Moreover, such spectral and remote sensing-based approaches help to decrease the impacts of early stages of exploration due to their less invasive nature. Therefore, we present a spectral library built with empirical data available for public use, focusing on Lithium minerals and pegmatites of the Barroso pegmatite field (Portugal), one of the largest hard-rock European Lithium deposits, built within the scope of the INOVMINERAL4.0 project (https://inovmineral.pt/). The authors acknowledge the support provided by Portuguese National Funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P. (Portugal) projects UIDB/04683/2020 and UIDP/04683/2020 (Institute of Earth Sciences); through ANI and COMPETE 2020 as well as European funds through the European Regional Development Fund (ERDF) with POCI-01-0247-FEDER-046083 INOVMINERAL project. The authors also thank Savannah Resources PLC for access to the Aldeia pegmatite and for providing the samples used in this study.

  • Chinese
    Authors: 
    Xianhua, Wang; Han, Ye Han;
    Publisher: Science Data Bank

    data about Cloud Detection Algorithm for Greenhouse Gas Retrieval

  • 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.

  • Open Access
    Authors: 
    Zatyagov, Denis; Killingseder,, Patrick; Kugu, Ozan; Reiterer, Bernhard; Wallner, Bernhard; Choubeh, Nima Rahmani;
    Country: Netherlands

    Virtual Micro Challenge 2022 is a part of the Industrial Mobile Manipulation Challenge (IMMC) - an international initiative funded by EIT-Manufacturing, aiming to promote mobile manipulation technology and make progress in the field of human-machine co-working in manufacturing. This repository contains software packages for Virtual Micro Challenge 2022 and it is supposed to be used by the participants and organizers.

  • Open Access German
    Authors: 
    Stewart, Benjamin; Reiners, Wulf;
    Publisher: Zenodo

    Wissen muss weltweit zugänglich sein, um die Krisen unserer Zeit - von Klima und Energie bis hin zu Gesundheit und Ernährungssicherheit - zu bekämpfen. Cloud-Server und satellitengestütztes Internet könnten umfänglichen Zugang ermöglichen. Neben technologischen Lösungen bedarf es jedoch politischen Willens, internationale Kooperationsstrukturen so auszurichten, dass sie die freie Verbreitung von Wissen nicht behindern. Horizon 2020 MSCA-RISE, Grant Agreement #873119

  • Chinese
    Authors: 
    Xiang, Zhang Ding; BAI Xiaofei; LI Yanan;
    Publisher: Science Data Bank

    Shrub land is the woodland with more than 40% shrub cover, including artificial shrub land and natural shrub land. Shrub land plays an important role in maintaining the country's ecological security. From the perspective of forest water conservation, shrub land has a high maximum and effective amount of dead litter, which plays an important role in water conservation. As an index of evaluating regional soil and water conservation capacity, its dynamic monitoring is of great significance to coordinate forestry land use and improve the soil and water conservation capacity according to local conditions. This paper uses the 30-m land use grid data in the 2020 National Land Change Survey and the kilometer grid production software, which is calculated in the 2020 grid space. This data set can be used for territorial spatial planning, forestry resource planning, ecological asset value estimation, forest carbon sink calculation, afforestation effect evaluation, and remote sensing sample database construction, etc.

  • Chinese
    Authors: 
    ZHANG Yucui; QI Yongqing; SHEN Yanjun; PAN Xuepeng;
    Publisher: Science Data Bank

    Mapping the agricultural land use of the North China Plain in 2002 and 2012

  • Chinese
    Authors: 
    Suying Chen; Xiying Zhang; Liwei Shao; Hongyong Sun; Junfang Niu; Xiuwei Liu;
    Publisher: Science Data Bank

    1、Experiment site and time:This study was conducted from October 2010 to June 2016 at theLuancheng Agro-Eco-experimental Station (37°53′ N, 114°40′ E; elevation 50 m) of the Chinese Academy of Sciences.2、Experimental design and field management:Four straw management practices were tested: (1) all the straw from the double-cropping system was left in the field after each harvest (as control, WMs); (2) maize straw was removed as yellow silage and winter wheat straw was left in the field (a practice to increase feed for the local dairy industry, abbreviated as Ws); (3) some manure produced from dairy farms was added to Ws (abbreviated as Ws + M); (4) extra manure was added to WMs (abbreviated as WMs + M).3.Parameter monitoring(1)Weather data:Daily weather data were obtained from a standard weather station approximately 100 m away from the experimental site and included the daily temperature, rainfall, radiation, humidity and wind speed.(2)Soil chemical and physical characteristics:Before and at the end of the test, soil chemical properties were sampled to determine available phosphorus, available potassium, total nitrogen and organic matter. The sampling depth was 0-20cm and 20-40cm.(3)Crop development, biomass and grain yield:Phenological developments were recorded based on the appearance date of 20%, 50% and 80% for the major growing stages of winter wheat and summer maize. Before harvesting winter wheat and summer maize, the spike numbers per unit area (SN) were counted in the field. Then, 80 plants (for winter wheat) and 4 plants (for summer maize) were collected from each plot to determine the kernel number per spike (KNS), kernel weight, thousand grain weight (TGW), total dry matter and harvest index (HI). For yield measurement, 10 m2 from each plot was harvested manually for winter wheat. All plants were anuallythreshed with a thresher. After obtaining the grains, the straw harvested from each plot was returned to the field. For maize, the ears from the plants were manually harvested and threshed to obtain the grains.Grains were air-dried to a constant moisture content (13%), and the weight was recorded to obtain the final grain yield. For the Ws treatment, all the above-ground biomass for maize was removed from the field after harvesting maize。(4)Soil water content, crop water use and use efficiency:Soil water content was regularly monitored using a neutron probe (503 DR, CPN International Inc., USA) down to 2 m by 20 cm increment. An access tube was installed in the center of each plot. The surface soil layer (0–20 cm) was regularly monitored by a portable TDRsensor (MP-160, Meridian).Seasonal crop water use, or evapotranspiration (ET), was calculated based on the water balance equation:WUE=y/ET,ET=P+I+SWD+R-D+CRwhere ET is seasonal evapotranspiration or crop water use (mm), P is precipitation (mm), I is irrigation (mm), SWD is soil water depletion for the top 2 m soil profile (mm), R is surface runoff (mm), D is drainage from the root zone (mm), and CR is capillary rise to the root zone (mm). Surface runoff was not observed due to the low rainfall, and the capillary rise was negligible due to the groundwater table being 40 m below the soil surface. D was calculated following the method used by Liu et al. (2013). Water use efficiency (WUE) in grain production (WUEg) was defined as grain yield divided by crop water use, and WUE in biomass production (WUEb) was defined as biomass divided by cropwater use.(5). Element contents in straw and manure:The contents of C, N, P and K in the straw were estimated for each season separately by using the biomass production that was measured every year for both winter wheat and summer maize. The total C and N of the straw was measured by the elementary analysis system GmbH(vario MACRO cube, Germany). The method used to measure the P and K contents was the same as used for the soil P and K. The same methods were used to measure the contents of C, N, P and K in the manure.

  • English
    Authors: 
    Hernández, Óscar G.; Lopez-Castellanos, Jose M.; Jara, Carlos A.; Garcia, Gabriel J.; Ubeda, Andres; Morell-Gimenez, Vicente; Gomez-Donoso, Francisco;
    Publisher: Science Data Bank

    Human Muscular Manipulability is a metric that measures the comfort of an specific pose and it can be used for a variety of applications related to healthcare. For this reason, we introduce KIMHu: a Kinematic, Imaging and electroMyography dataset for Human muscular manipulability index prediction. The dataset is comprised of images, depth maps, skeleton tracking data, electromyography recordings and 3 different Human Muscular Manipulability indexes of 20 participants performing different physical exercises with their arm. The methodology followed to acquire and process the data is also presented for future replication. A specific analysis framework for Human Muscular Manipulability is proposed in order to provide benchmarking tools based on this dataset.

  • Open Access
    Authors: 
    Kitambo, Benjamin; Papa, Fabrice; Paris, Adrien; Tshimanga, Raphael M.; Frappart, Frederic; Calmant, Stephane; Elmi, Omid; Fleischmann, Ayan Santos; Becker, Melanie; Tourian, Mohammad J.; +2 more
    Publisher: Zenodo

    1. Summary The Congo basin’s Surface Water Storage (SWS) datasets are generated by Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, Sly Wongchuig in the article entitled "A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015", Earth System Science Data (submitted). The dataset was generated using two methods, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of surface water extent (SWE) from the Global Inundation Extent from Multi-satellite (GIEMS-2) and satellite-derived surface water height (SWH) from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from GIEMS-2 dataset and hypsometric curves obtained from various digital elevation models (DEMs) (i.e., ASTER, ALOS, MERIT, and FABDEM). Both methods estimate monthly spatio-temporal variations of SWS changes across the entire Congo River basin. 2. Name Description HYPSO_XX: hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). HYPSO_CORR_XX: corrected hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). AREA_STOR_XX: hypsometric curve providing the surface water extent area-storage relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs). SWS_XX: monthly surface water storage variations from XX (where XX stands for ASTER, ALOS, MERIT, FABDEM DEMs, and Multi-satellite approach). 3. File Description The SWS estimates from the multi-satellite approach (1995-2015), as well as the hypsometric curves providing the surface water extent area-elevation relationship from the four DEMs (before and after the corrections), the surface water extent area-storage relationship, along with the four SWS estimates (1992-2005). The dataset is gridded on equal-area of 0.25° spatial resolution at the equator, each pixel covers almost 773 km². The reference point for calculating the volume variation is the minimum of surface water extent for each pixel. The files are organized in matrix: First column represents the latitude in degree. Second column represents the longitude in degree. From the third column: data. In case of hypsometric curve, the data represents elevation in meter on 101 columns representing the increment of 1% flooding in each 773 km2 pixel from GIEMS-2. In case of SWS data (in km³), there are 288 (respectively 252) columns representing each month of the period over 1992-2015 (respectively 1995-2015) from hypsometric curve approach (respectively multi-satellite approach).