Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
The following results are related to Rural Digital Europe. Are you interested to view more results? Visit OpenAIRE - Explore.
61 Research products, page 1 of 7

  • Rural Digital Europe
  • Research data
  • 2022-2022
  • Dataset
  • NARCIS
  • DataverseNL

10
arrow_drop_down
Date (most recent)
arrow_drop_down
  • Open Access
    Authors: 
    Mens, Lotte; Bargues-Tobella, Aida; Sterck, Frank; Vågen, Tor-Gunnar; Winowiecki, Leigh Ann; Lohbeck, Madelon;
    Publisher: Wageningen University & Research
    Country: Netherlands

    We measured field-saturated soil hydraulic conductivity (Kfs) and soil organic carbon (SOC) in 38 plots across agricultural landscapes in Muminji, Kenya. Woody vegetation and land use inventories took place and species functional traits were measured on the 63 most important species. We systematically tested the effects of vegetation quantity (aboveground woody  biomass and vegetation cover) and quality (functional properties and diversity) on soil health (Kfs as a proxy for soil infiltrability and SOC for soil fertility).  This dataset contains the data that underlies the analysis and outcomes of this study, described for each dataset below.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Larsen, Kristian Weber;
    Country: Netherlands

    Archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments where a drone (CrazyFlie001) is crashed into a rigid wall (ImpactPlane001). In these recordings, CrazyFlie001 slides down from a slide, enters free flight, and then crashes into a wall. The purpose of these experiments is to perform velocity based parameter identification of the contact parameters in a nonsmooth modeling framework. The involved contact is between the object and the environment, which in these recordings are a drone (CrazyFlie001) and a rigid wall (ImpactPlane001), respectively. All the recordings in the archive were performed at the Robotics field of the Robotics Lab, located in the Impuls building at the TU/e campus. More information about the dataset, the objects, and the environments used, can be found on https://impact-aware-robotics-database.tue.nl/.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Dingemans, Sander; van Gorp, Teun;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, a UR10 robot is used to toss and Box005 on a stationary and a moving conveyor belt. The purpose of these experiments is perform a repeatability analysis of tossing boxes as part of the modeling framework. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu) to predict the end pose of a certain box on a conveyor belt, after it is tossed. This means that the involved contact is between the object and the environment, which in these recordings are Box005 and a conveyor, respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information can be found on https://impact-aware-robotics-database.tue.nl/.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Dingemans, Sander;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, Box005 and Box006 are manually tossed on a stationary and a running conveyor. The purpose of these experiments is to validate the box-conveyor impact modeling framework to assess how well it can predict the rest-pose of the box. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu). The involved contact is between the object and the environment, which in these recordings are Box005 and Box006 and a conveyor (Conveyor002), respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information about the dataset, the objects, and the environments used, can be found on https://impact-aware-robotics-database.tue.nl/.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Dingemans, Sander; van Gorp, Teun;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, a UR10 robot is used to toss Box005 on a stationary and a moving conveyor belt, where the box is picked up from different pick-up positions. The purpose of these experiments is to perform a sensitivity analysis of tossing boxes as part of the modeling framework to assess how sensitive the rest-pose of the box is to varying pick-up positions. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu) to predict the end pose of a certain box on a conveyor belt, after it is tossed. The involved contact is between the object and the environment, which in these recordings are Box005 and a conveyor, respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information can be found on https://impact-aware-robotics-database.tue.nl/.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Dingemans, Sander;
    Country: Netherlands

    I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments where Box006, Box007, and Box009 are tossed on a stationary and a running conveyor, with a black background and an industrial background. The purpose of these experiments is to validate the impact-aware object tracking algorythm for tossed boxes in logistic environments. The involved contact is between the object and the environment, which in these recordings are Box006, Box007, and Box009 and a conveyor (Conveyor002), respectively. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus. More information about the dataset, the objects, and the environments used, can be found on https://impact-aware-robotics-database.tue.nl/.

  • Open Access
    Authors: 
    Jongeneel, Maarten; Saccon, Alessandro; Voorst, van, Job; Lubbers, Menno;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    I.AM. archive as part of the Impact-Aware Robotics Archives Collection. This archive contains recordings of experiments that are executed under the scenario of TOSS. In these recordings, a UR10 robot is used to release and drop a plastic plate with different weights attached on a conveyor belt. The purpose of these experiments is to learn the release dynamics of the suction cup. This modeling framework is used within the H2020 I.AM. project (www.i-am-project.eu) to predict the end pose of a certain box on a conveyor belt, after it is tossed. Within these experiments, the involved contact transition is a release between the robot and the object. All the recordings in the archive were performed at the Innovation Lab of Vanderlande, located within the TU/e campus.

  • Open Access
    Authors: 
    de Croon, Guido; Dupeyroux, Julien; de Wagter, Christophe; Chatterjee, Abhishek; Olejnik, Diana A.; RUFFIER, Franck;
    Publisher: 4TU.ResearchData
    Country: Netherlands

    This dataset contains the MATLAB code and experimental data for reproducing the observability analysis, simulation experiments, and plotting of robotic results for the article:  “Accommodating unobservability to control flight attitude with optic flow”, by G.C.H.E. de Croon, J.J.G. Dupeyroux, C. De Wagter, A. Chatterjee, D.A. Olejnik, F. Ruffier. The dataset contains the logged flight data from quadrotor experiments (with a Parrot Bebop 2) and flapping wing robot experiments (with a Flapper Drone), as performed for the paper mentioned above. Moreover, it contains the post-processed data (positions, pitch attitudes, as extracted with computer vision scripts from the images captured in the experimental setup) from the honeybee experiments performed in the article: Portelli, G., Ruffier, F., Roubieu, F. L., & Franceschini, N. (2011). Honeybees' speed depends on dorsal as well as lateral, ventral and frontal optic flows. PloS one, 6(5), e19486. This data has been re-analyzed in the light of the proposed new theory on estimating attitude with the help of optic flow. The dataset contains a README document that further explains how to reproduce the results with the MATLAB code.  

  • 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: 
    de Aquino, Sinara; Kiwuka, Catherine; Tournebize, Rémi; Marraccini, Pierre; Mariac, Cédric; Bethune, Kévin; Couderc, Marie; Cubry, Philippe; Andrade, Alan; Lepelley, Maud; +10 more
    Publisher: Dryad
    Country: Netherlands

    Study species and sample selection: Uganda is divided into sixteen climate zones based on precipitation patterns as defined by Basalirwa (1995), five of which host C. canephora stands. Within these five climate zones, 207 georeferenced trees were sampled from seven wild forests in 2012 and 2014 by the National Agricultural Research Organization (NARO, Uganda) and collaborators of the Institut de Recherche pour le Développement (IRD, Montpellier, France). These forests include: Budongo (n=65), Itwara (n=23), Kibale (n=19), Kalangala (n=10), Mabira (n=25), Malabigambo (n=16) and Zoka (n=49). Populations in Zoka, Budongo, Kalangala, Mabira and Malabigambo occurred in distinct climatic envelopes, while the climatic envelopes in Itwara tended to overlap those of Kibale (Kiwuka et al., 2021). In each targeted forest, leaf samples were collected from five sub-sites that were separated by distances of at least 5 km. Selection of candidate genes and bait design: The 323 candidate genes (CGs) selected for the present study have been annotated and/or functionally characterized in previous studies. They all code for candidate proteins already reported to play important roles in central metabolism or in plant responses and adaptation to abiotic stress. The CG sequences were retrieved from the whole genome assembly of C. canephora (Denoeud et al., 2014) according to the annotation available on the Coffee Genome Hub (http://coffee-genome.org/) (Dereeper et al., 2015). Probes were designed to cover each CG coding region as well as 1 kb upstream and 500 bp downstream flanking regions, so as to include putatively regulatory regions. The 120 bp MyBaits® probes were designed with 2X tiling and synthesized by MYcroarray provider (Ann Arbor, Michigan, USA). A total of 21,306 probes were designed. Each candidate probe was BLASTed against the C. canephora genome (Denoeud et al., 2014) and filtered based on the manufacturer’s stringent criteria (Mariac et al., 2022). Library preparation and sequencing: DNA extractions for the 207 samples were performed at the IRD facilities from silica-gel dried leaves according to a previously described protocol (Mariac et al., 2006). Genomic libraries were constructed using the protocols outlined in Rohland & Reich (2012) and Mariac et al. (2014). The 207 individual libraries were then capture-enriched by pools of 48 libraries using the synthetic RNA MyBaits® probes and according to the MYcroarray protocol (Mariac et al., 2022). The enriched pools were quantified using real-time PCR and combined in equimolar ratios prior to sequencing on one lane of 150 bp paired end reads on an Illumina HiSeq 3000 sequencer (GeT-PlaGe Platform, GenoToul, Toulouse, France). SNP genotyping, calling and filtering: Sequence analysis was performed using scripts published by Mariac et al. (2014) and Scarcelli et al. (2016) and also available on GitHub (https://github.com/Maillol/demultadapt; https://github.com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality.pl). The mapping step was carried out using BWA MEM 0.7.5a-r405 (Li & Durbin, 2009) with the default option (-B 4) and the C. canephora assembly (http://coffee-genome.org/coffeacanephora) as reference. SNP calling was done using UnifiedGenotyper in the Genome Analysis Toolkit (GATK v3.6). SNPs located on the selected CG sequences were considered as ‘in-target’ and the other ones as ‘off-target’. Two successive sets of filters were applied to raw SNPs. We first discarded low quality variants according to the quality criteria recommended by GATK, and selected only biallelic SNPs using VCFtools v0.1.13 (Danecek et al., 2011). We applied additional filters for population genetic analyses and for association analyses, i.e. keeping SNPs with no excess of heterozygous genotypes (< 0.8), a minor allele frequency (MAF) greater than 5% and under linkage equilibrium. For the latter filter, SNPs were processed with PLINK 1.90b4 (Purcell et al., 2007) to prune only SNPs in approximate linkage equilibrium based on the pairwise correlation between the SNP genotype counts for 100 bp sliding windows with 10 bp steps (option -indep-pairwise). The SNPs were considered correlated when r2 > 0.5. These filters led to a total of 5,860 SNPs: 4,753 in-target and 1,107 off-target loci. Bioclimatic data: Environmental factors (bioclimatic variables BIO1-19, Table S1) were downloaded from the WorldClim database (http://www.worldclim.org, Fick & Hijmans, 2017) at 30 arc-second resolution (~1 km) for ‘Current conditions ~1960-2000’ Understanding vulnerabilities of plant populations to climate change could help preserve their biodiversity and reveal new elite parents for future breeding programs. To this end, landscape genomics is a useful approach for assessing putative adaptations to future climatic conditions, especially in long-lived species such as trees. We conducted a population genomics study of 207 Coffea canephora trees from seven forests along different climate gradients in Uganda. For this, we sequenced 323 candidate genes involved in key metabolic and defense pathways in coffee. Seventy-one SNPs were found to be significantly associated with bioclimatic variables, and were thereby considered as putatively adaptive loci. These SNPs were linked to key candidate genes, including transcription factors, like DREB-like and MYB family genes controlling plant responses to abiotic stresses, as well as other genes of organoleptic interest, like the DXMT gene involved in caffeine biosynthesis and a putative pest repellent. These climate-associated genetic markers were used to compute genetic offsets, predicting population responses to future climatic conditions based on local climate change forecasts. Using these measures of maladaptation to future conditions, substantial levels of genetic differentiation between present and future diversity were estimated for all populations and scenarios considered. The populations from the forests Zoka and Budongo, in the northernmost zone of Uganda, appeared to have the lowest genetic offsets under all predicted climate change patterns, while populations from Kalangala and Mabira, in the Lake Victoria region, exhibited the highest genetic offsets. The potential of these findings in terms of ex-situ conservation strategies are discussed.