dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We acknowledge the  Servicio Meteorológico Nacional ,  Instituto Nacional de Tecnología Agropecuaria, Secretaría de Recursos Hídricos, Autoridad Interjurisdiccional de Cuencas, Dirección Provincial del Agua in Argentina, Administracion de Parques Nacionales Argentina, Estancia Los Huemules, Dirección General de Aguas and Dirección Meteorológica de Chile in Chile, and Servicio Nacional de Meterología e Hidrología in Bolivia and Perú  for providing quality-controlled daily and monthly climate data, which were essential for developing the reconstruction models. This study was supported by the  Agencia Nacional de Promoción Científica y Tecnológica, Argentina  ( PICT 2013-1880 ),  Consejo Nacional de Investigaciones Científicas y Tecnológicas  ( PIP 11220130100584 ) projects. The  Inter-American Institute for Global Change Research   CRN03 ,  CRN2047  and  CRN027  funded part of this study through grants ( NSF  Grant  GEO-0452325 ).
3 fold, similarly to previously reported data in the study by Vasquez et al. [11]. This improvement was particularly evident for samples collected in the first trimester of pregnancy. Such a high gain in sensitivity probably reflected the high proportion of sub-microscopic infections at this period. Only 28 (41%) out of the 68 samples which were positive by qPCR but negative by uRDT had detectable levels of HRP2. However, the HRP2 concentrations were very low, explaining the negative uRDT result. Compared to samples which were negative both by cRDT and uRDT, higher HRP2 levels were detected in samples which were positive by uRDT but not by cRDT (5. 5-fold increase), and positive by both RDTs (146-fold increase). HRP2 was detected in 48 of the 49 samples that were positive by uRDT but negative by qPCR.
It regroups a wide array of RNAseq studies, and allows to search for expression patterns of all tomato genes. For banana, there is the Banana Genome Hub, containing a transcriptomics search tool  (Droc et al. , 2013). For apple, there is a similar web site, called AppleMDO, published recently by  Da et al. (2019). For collecting grape RNAseq data and making them searchable, a new platform has been created, called Grape-RNA  (Wang et al. , 2020)  and for Citrus sinensis a recent data basis has been created too  (Feng et al. , 2019). Mining such databases and others, for other fruit species, with a focus on the few starch synthesis genes, by comparing developing stages of climacteric and non-climacteric fruit, would probably generate new insight into differences between these two fruit classes, and may reinforce the fact that starch should be a cornerstone of the definition of climacteric vs.
The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1. 0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) , and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014-2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1. 0 product, with a focus on the transition from SPOT/VGT to PROBA-V.
The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1. 0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1. 0. C3S PROBA-V SA v1. 0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1. 0 (2-3%), but C3S PROBA-V SA v1. 0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.
The European's Union funded C3S  [2]  provides key indicators on climate change drivers by means of combining the observations of the climate system with the latest science to develop authoritative, quality-assured information about the past, current and future states of the climate. The C3S portfolio includes consistent estimates of multiple Essential Climate Variables (ECVs), including the surface albedo, which is primarily based on satellite data and provided as a series of gridded CDRs. In the frame of C3S, SA products have been derived from the data collected by NOAA/AVHRR  and SPOT/VGT  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014). The Project for Onboard Autonomy satellite (PROBA-V) satellite  [3] , which was launched on May 2013 for seven years, was designed to bridge the gap in space-borne vegetation observation between SPOT/VGT (March 1998-May 2014) and the Sentinel-3 satellites.
PROBA-V is comparable to the VGT sensors on SPOT platforms. In C3S, the PROBA-V SA v1. 0 products are generated with the aim to extend the CDRs based on AVHRR and VGT observations over time. The algorithms for these C3S SA products were designed by Meteo-France based on previous research conducted within the Satellite Application Facility for Land Surface Analysis (LSA SAF) program of EUMETSAT  [4, 5]. Meteo-France developed the SA algorithms, Refs. [6] [7] [8] [9]  based on MSG/SEVIRI and MetOp/AVHRR within the EUMETSAT LSA SAF program, and were later adapted to these other sensors in C3S and Copernicus Global Land Service (CGLS)  [10]. These products are generated by the processing line infrastructure implemented by VITO and openly distributed through the C3S Climate Data Store (CDS)  [11].
Additionally, two ancillary variables were also taken into account: The uncertainty (ERR) and the mean age (AGE, in number of days) of the observations that are used to produce the SA. The SPOT/VGT SA v1. 0 pixels with associated uncertainty greater than 0. 2 and an AGE greater than 20 were discarded. For MODIS C6, the quality flags are given in the MCD43A2 product, and those pixels with fewer than seven valid observations were discarded. 2. 1. Evaluated Dataset: C3S SA v1. 0 Based on PROBA-V Data PROBA-V has operated since May 2013 at an altitude of 820 km in a sun-synchronous orbit with a local overpass time at launch of 10:45 a. m. However, because the satellite has no onboard propellant, this local overpass time gradually differs from the at-launch value  [3]. The payload consists of three identical cameras, which are equipped with a very compact Three Mirror Anastigmat telescope.
NASA MCD43 C6 Based on MODIS Data The MODIS BRDF/Albedo (MCD43A3) Collection 6 (C6) dataset, which is available from  [47] , provides both directional albedo at local solar noon and bi-hemispherical albedo for MODIS bands 1-7 and for three broadbands (visible [0. 3-0. 7 µm], NIR [0. 7-5. 0 µm], and total shortwave [0. 3-5. 0 µm]). The MCD43A3 albedo quantities are delivered at a resolution of 500 m in a sinusoidal projection. They have been produced every day since 2000 with a synthesis period of 16 days. Data from both Terra and Aqua satellites are used in the generation of this product. The MODIS albedo algorithm uses atmospherically corrected reflectance data (MOD09 product) to establish the best fit to a linear kernel-driven BRDF model, with the exception of the observations flagged as "cloud", "cirrus high" or "aerosol high".
The broadband albedos are computed using the spectral to broadband conversion approach  [50]. The MCD43 C6 products use an improved back-up database, which is pixel-based updated from the latest full inversion as opposed to the land cover-based database used in the previous Collection 5. MCD43A3 SA products have reached CEOS LPV validation stage 3  [51]. Sánchez-Zapero et al. [46]  reported an overall uncertainty (RMSD) of 0. 053 and a low negative bias (-11. 9%) of MCD43A3 C6 compared with the ground data from 15 FLUXNET homogeneous sites for the 2000-2005 period (653 samples), and a slight overall uncertainty (RMSD = 0. 032) was reported compared with European FLUXNET measurements over snow-free conditions  [52]. Existing studies of the previous collection 5 indicate that the accuracy of the MODIS shortwave broadband albedo meets the requirements (<5%) for both snow-free and snow-covered surfaces  [31] [32] [33] [34].
Ground-Based Observations for Validation (GBOV) Database The CGLS GBOV  [53]  initiative aims at facilitating the use of observations from operational ground-based monitoring networks and their comparison to Earth Observation (EO) products. In the case of the SA, GBOV provides the Reference Measurements (RMs) and upscaled Land Products (LPs) based on the RM data  [54, 55]  from over 24 sites coming from different networks, such as BSRN, FLUXNET, or SURFRAD. Currently, the GBOV database is made available for the 2012-2018 period  [53]. The RM GBOV data corresponds to blue-sky albedo, which is defined as the fraction between the downward and the upward shortwave radiative flux. The diffuse fraction is also included in the RM dataset. The innovative approach for the generation of GBOV LPs could be very useful for the validation of satellite SA products since it can be applied to both homogeneous and heterogeneous land surfaces.
However, we can expect large discrepancies over heterogeneous sites compared to homogeneous sites  [54, 56]. The CEOS LPV albedo validation protocol  [21]  recommends the use of ground values measured at the flux tower (i. e. , the RM) for the direct validation of EO products. For those reasons, GBOV RMs are used in this study for the accuracy assessment. Note that RMs are provided daily in the GBOV database with a typical temporal step of 30 or 1 min depending on the station. The estimation of the uncertainty associated with a solar radiation measurement by a commercial pyranometer is around 5% (at a 95% confidence level) for daily values under ideal conditions  [57]. Since satellite products are defined to provide SA retrievals at solar local noon, the RMs used in this study have been taken at the same time.
Satellite Product Intercomparison The intercomparison of satellite products must account for the differences in the spatial and temporal characteristics of the different datasets. Consequently, a spatial support area and temporal support period were defined consistent with the C3S SA characteristics (Table  1 ). Thus, the MCD43A4 C6 products were re-sampled to Plate Carrée using a 1 km spatial sampling grid. Satellite products were compared using the closest date of their different temporal composites, using the 10 day temporal frequency of C3S SA products. The completeness and precision of the different products were evaluated and compared, as well as the temporal consistency among the several products, which was qualitatively assessed per biome type. The uncertainty estimates from the intercomparisons provided an overall figure of the spatio-temporal consistency between products.
The next step is the careful selection of the homogeneous sites, which are similar to the footprints of the satellite pixel resolution of interest. The accuracy assessment of the SA satellite products was performed against the measurements coming from the 20 selected GBOV homogeneous stations presented in Section 3. The study here was extended to the period from 2014 to 2018 to have the maximum concomitant samples from the GBOV dataset to PROBA-V observations. The accuracy assessment of MCD43A3 C6 for the same period and sampling was performed for benchmarking. This exercise was carried out at a resolution of 1 km, which is equivalent to one pixel in the case of PROBA-V SA v1. 0, and averages of 2 × 2 pixels in the case of MCD43A3 C6. The 10 day frequency of C3S products was used as temporal sampling, and the average values of the daily ground data were computed to compare with satellite estimations during the corresponding temporal composite window of each product (see Table  1 ).
5. 1. 3. Spatio-Temporal Consistency. The overall spatio-temporal consistency between PROBA-V SA v1. 0 and the reference products is assessed over the LANDVAL network sites considering all good quality observations according to In the case of PROBA-V, filled dots correspond to 'good quality' pixels, and unfilled dots correspond to pixels flagged as 'low-quality' according to QFLAG (Table  3 ). For evergreen broadleaved forests, some temporal noise was observed in all satellite products. However, PROBA-V SA v1. 0 and MCD43A3 C6 seem to provide less noise (i. e. , flatter temporal trajectories) than SPOT/VGT SA v1. 0, which seems to be more realistic for this biome type. For the rest of the biomes, the PROBA-V SA v1. 0 profiles follow the temporal trends of SPOT/VGT SA v1. 0 and MCD43A3 C6. The presence of rapid changes due to snow events, the occurrence of stable profiles and the phenological changes were consistent among the three datasets.
0, C3 SPOT/VGT SA v1. 0 and MCD43A3 C6) starting from the different input data and different atmospheric correction methods; including the BRDF parameterization, temporal compositing and angular integration; and finalizing with the narrow to broadband conversion. Regarding the input data, each sensor works in different spectral channels (see Table  2 ). The SPOT/VGT and PROBA-V channels provide very similar spectral characteristics in the Blue, Red and NIR bands, whereas significant differences are found for the SWIR channel. The central wavelengths of the MODIS spectral bands are comparable to the corresponding bands for PROBA-V and VGT, albeit the MODIS spectral bands are narrower. These differences could translate into reflectance discrepancies in regions with high absorption features like in the visible domain, where large uncertainty values were found between pairs of products.
Thus, fiducial reference measurements must be characterized with highly calibrated instrumentation at dedicated cal/val sites to better estimate the satellite product uncertainty budget. The compliance of satellite EO products versus ground data with user requirements showed a low percentage of pixels within GCOS (only <25%) and C3S (<50%) requirements, which indicates the difficulties of achieving these requirements using current products. To further improve the compliance with requirements, it is recommended that EO programs provide the uncertainties associated with the processing chain, mainly related to the sensor calibration and atmospheric correction. In that way, the steps providing higher error can be improved. However, the comparison between satellite C3S SA v1. 0 products showed an overall good spatio-temporal consistency in the comparison of SPOT/VGT versus PROBA-V during the 6-month overlapping period (December 2013-May 2014), with typically more than 60% of the samples within the C3S target requirements for the visible domain, and more than 75% of the samples within the requirements for the NIR and total shortwave.
Conclusions This paper presents the quality assessment results of C3S PROBA-V SA v1. 0 products (broadband albedos) through intercomparison with reference satellite products (C3S SPOT/VGT SA v1. 0 and MCD43 C6) at the global scale and the direct validation with a representative amount of ground data (1715 samples) across different biomes types (eight stations over forests, six over crops, three over grassland/shrubs, and three over desert). This validation exercise is a novelty in the literature, since very few global and temporally representative SA validation exercises have been published  [21] , and they are mainly based on MODIS observations  [15] [16] [17]. The validation methodology adopted the guidelines, protocols and metrics defined by the CEOS LPV best practices for the validation of global albedo satellite products  [21].
• The accuracy of the C3S PROBA-V SA v1. 0 best quality retrievals with respect to the ground data over a five-year period (2014-2018) showed systematic positive overestimation, which was mainly observed for the lowest albedo ranges (SA < 0. 2) over forest sites. Similar uncertainty (RMSD∼ 0. 4) was found for MCD43A3 C6 products using the same sampling, showing the opposite sign for the mean bias.
Funding: This work was funded by the  European Centre for Medium-Range Weather Forecasts  in the framework of the  Copernicus Climate Change Service  (Official reference number service contract:  2018/C3S_312b_Lot5_VITO/SC1 ), led by  VITO  as the prime contractor. Previously, the development of the algorithm and the methodology were defined in the framework of the  Copernicus Global Land Service (European Commission/Joint Research Center under Framework Service  Contract  N • 199494 ). Acknowledgments: This study has been undertaken using data from GBOV " Ground Based Observation for Validation " (https://land. copernicus. eu/global/gbov)founded by the  European Commission Joint Research Centre   FWC932059  as part of the  Global Component of the European Union's Copernicus Land Monitoring Service.
Hydro-climatological study is difficult in most of the developing countries due to the paucity of monitoring stations. Gridded climatological data provides an opportunity to extrapolate climate to areas without monitoring stations based on their ability to replicate the Spatio-temporal distribution and variability of observed datasets. Simple correlation and error analyses are not enough to predict the variability and distribution of precipitation and temperature. In this study, the coefficient of correlation (R 2 ), Root mean square error (RMSE), mean bias error (MBE) and mean wet and dry spell lengths were used to evaluate the performance of three widely used daily gridded precipitation, maximum and minimum temperature datasets from the Climatic Research Unit (CRU), Princeton University Global Meteorological Forcing (PGF) and Climate Forecast System Reanalysis (CFSR) datasets available over the Niger Delta part of Nigeria.
The Standardised Precipitation Index was used to assess the confidence of using gridded precipitation products on water resource management. Results of correlation, error, and spell length analysis revealed that the CRU and PGF datasets performed much better than the CFSR datasets. SPI values also indicate a good association between station and CRU precipitation products. The CFSR datasets in comparison with the other data products in many years overestimated and underestimated the SPI. This indicates weak accuracy in predictability, hence not reliable for water resource management in the study area. However, CRU data products were found to perform much better in most of the statistical assessments conducted. This makes the methods used in this study to be useful for the as-.
Introduction The accuracy and reliability of climate datasets are crucial for scientific research and hydrologic studies related to climate change impact assessment, numerical weather prediction, flood forecasting, drought monitoring or water resources management  [1]. Paucity of data remains a challenging task especially in developing countries and in remote parts of the world where ground-based precipitation measurements, such as radar networks and rain gauges are either sparse or non-existent due to the high cost of establishment and maintenance of the infrastructure  [2]. Recently, climate impact studies have become increasingly detailed as global industrialisation results in an unprecedented increase in greenhouse gases concentrations (GHGs), and associated impact on the changing climate  [3].
Climate change impact assessment requires climate data at various spatial and temporal scales  [4]. Getting data observations at an acceptable spatial resolution is challenging in developing countries, and where available, their quality is either poor or expensive and may poorly represent a study area with large hydroclimatic gradients  [5]   [6]. To overcome these challenges, researchers resort to the use of multilayer global gridded representations of meteorological data to serve as inputs into climate and hydrological modelling studies  [6]. Climate datasets are typically measured in three ways viz: Gauge-Based observations, Satellite Estimates, and Reanalysis datasets  [7]. Gauge observations provide relatively accurate and trusted measurements at a single point and are subject to limitations, such as reporting time delays, sparse gauge networks, data gaps, unavailable over many sparsely populated and oceanic areas and limited access to available data  [8].
Satellite observations provide broad coverage of global atmospheric parameters with adequate spatial and temporal resolution in un-gauged regions, such as the oceans, complex mountain areas, and deserts. They provide information at regular intervals with uniform spatial coverage but contain non-negligible biases and random errors owing to the complicated nature of the relationship between the observations, sampling, and deficiencies in the algorithms. Reanalysis datasets merge random observations and models that encompass many physical and dynamical processes to generate a synthesised estimate of the state of the system across a regular grid, with spatial homogeneity, temporal continuity, and a multidimensional hierarchy  [7]. These datasets are Atmospheric and Climate Sciences classified into regional and global datasets.
Regional products are explicitly developed for countries or regions, for example,  [9]  and  [10]  developed climate datasets for Asia, and  [11]  developed for Europe,  [12]  for Spain, and,  [13]  for Switzerland. Different organizations have developed different global datasets such as, the Climatic Research Unit (CRU)  [14] , Global Historical Climatology Network (GHCN)  [15] , National Oceanic and Atmospheric Administration (NOAA)  [16] , Climate Forecast System Reanalysis (CFSR)  [17] , Princeton University Global Meteorological Forcing dataset (PGF)  [18]  and Global Precipitation Climatology Centre (GPCC)  [19]. These global gridded datasets have different spatial and temporal resolutions. The quality is governed by the data assimilation system, its optimisation and the quality of observation data used  [1]   [8].
The reliability of the gridded datasets varies with time and regional climate  [7]. This makes it necessary to evaluate the capability of gridded data before its application over specific locations. Recent studies show an increase in the usage of station gauge-based datasets for validating estimates from different interpolated datasets, reanalysis datasets, satellite products and climate models. For example,  [5]  used gauge-based datasets for comparison of four gridded datasets namely Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE), Global Precipitation Climatology Centre (GPCC), Centre for Climatic Research-University of Delaware (UDel); and Climatic Research Unit (CRU) datasets at stations located in the arid, semi-arid, and hyper-arid regions of Balochistan province of Pakistan.
[20]  used unified rain gauge data with three daily gauge based gridded rainfall datasets, namely the Indian Meteorological Department (IMD), APHRODITE and Climate Prediction centre (CPC) over India. [21]  used IMD dataset for comparison of two satellite datasets, namely, GPCP and Tropical Rainfall Measurement Mission (TRMM)  [22]. Compared station datasets with two gridded rainfall datasets, namely Climatic Research Unit (CRU), Hydro-Sciences Montpellier (SIEREM) for Burkina-Faso in West Africa. Considering such diverse use, it is necessary to carefully examine and compare the characteristics and pattern of the gridded datasets. An inter-comparison should analyse the prominent precipitation spell characteristics such as wet and dry spells among the different gridded datasets and importantly, the implications of using these gridded products on water resource management.