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2,452 Research products, page 1 of 246

  • Rural Digital Europe
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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.

  • 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

  • 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).

  • Open Access English
    Authors: 
    Zhang, Ke; Liu, Linxin;
    Publisher: Zenodo

    This dataset archived the monthly GPP and ET data for Yunnan Province of China from from 2000 to 2018 using the MOD17A2 GPP algorithm and the P-LSH ET retrieval algorithm.

  • Open Access
    Authors: 
    Niksa Alfirevic;
    Publisher: Zenodo

    This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.

  • Open Access
    Authors: 
    Niksa Alfirevic;
    Publisher: Zenodo

    This file is a detailed Research & Innovation Digitalization Scoreboard for the SEA-EU alliance and its member universities, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.

  • Open Access
    Authors: 
    Mosaffa, Hamidreza; Filippucci, Paolo; Massari, Christian; Ciabatta, Luca; Brocca, Luca;
    Publisher: Zenodo

    SM2RAIN-Climate rainfall product is a new long-term global scale rainfall product developed by using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product v06.1 as input into the SM2RAIN algorithm (Brocca et al., 2014; 2019). The SM2RAIN-Climate global rainfall dataset is generated in the period 1998-2021 with monthly temporal and 1° spatial resolutions, which provide the opportunity for climatological studies. Four different SM2RAIN-Climate datasets are provided in NetCDF format. For each dataset, the spatial grid (latitude and longitude), the rainfall values, and the mask type is defined in each NetCDF file. Two different masks are the temperature mask in data post-processing and a threshold value (percentage of missing data) taking into account missing data within a month. Depending on the application, the user can select the more suitable product. Acknowledgements The work is supported by the Open-Earth-Monitor Cyberinfrastructure project that has received funding from the European Union's Horizon Europe research and innovation programme (grant agreement no. 101059548) and by the European Space Agency through the Digital Twin Earth Hydrology project (grant no. ESA 4000129870/20/I-NB - CCN N. 1) and the 4DMED Hydrology project (grant no. ESA 4000136272/21/I-EF). {"references": ["Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489.", "Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Sch\u00fcller, L., Bojkov, B., Wagner, W. (2019). SM2RAIN-ASCAT (2007-2018): global daily satellite rainfall from ASCAT soil moisture. Earth System Science Data, 11, 1583\u20131601, doi:10.5194/essd-11-1583-2019. https://doi.org/10.5194/essd-11-1583-2019."]}

  • Open Access
    Authors: 
    Niksa Alfirevic;
    Publisher: Zenodo

    These files represent the exported WoS and Scopus records, used in the output D2.2 Digital transformation of research and innovation roadmap of the Horizont project reSEArch-EU, implemented by the SEA-EU university alliance.

  • 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

  • Restricted English
    Authors: 
    Mitchell, Frost; Aniqua Baset; Kasera, Sneha Kumar; Bhaskara, Aditya;
    Publisher: Zenodo
    Project: NSF | PAWR Platform POWDER-RENE... (1827940)

    Dataset Description This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters. The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus. Dataset Description Sample Count Receiver Count No-Tx Samples 46 10 to 25 1-Tx Samples 4822 10 to 25 2-Tx Samples 346 11 to 12 The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows: \(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \) Measurement Parameters Description Frequency 462.7 MHz Radio Gain 35 dB Receiver Sample Rate 2 MHz Sample Length N=10,000 Band-pass Filter 6 kHz Transmitters 0 to 2 Transmission Power 1 W Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device. Usage Instructions Data is provided in .json format, both as one file and as split files. import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) The json data is a dictionary with the sample timestamp as a key. Within each sample are the following keys: rx_data: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name. tx_coords: A list of coordinates for each transmitter. Each entry contains latitude and longitude. metadata: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in tx_coords File Separations and Train/Test Splits In the separated_data.zip folder there are several train/test separations of the data. all_data contains all the data in the main JSON file, separated by the number of transmitters. stationary consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers. train_test_splits contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with unused.json, is equivalent to the file all_data/single_tx.json. The random split is a random 80/20 split of the data. special_test_cases contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus. The grid split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the grid split. The seasonal split contains data separated by the month of collection, in April or July. The transportation split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The non-driving.json file contains the union of the walking and cycling data. campus.json contains the on-campus data, so is equivalent to the union of each split, not including unused.json. Digital Surface Model The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR survey. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models. To read the data in python: import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') Dataset Acknowledgement: This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product. DSM DOI: https://doi.org/10.5069/G9TH8JNQ