dataesr/scientific-paragraphs-categorization huggingface dataset:
Variations in frequency, length and intensity of dry and wet spells within predicted datasets often lead to faulty hydrological and agricultural decisions like extreme events estimation, improper selection of crops, incorrect estimation of sowing and harvesting time  [23]. Wet spells are prolonged number of wet days and serve as an indicator of flood conditions while Dry spells are prolonged period of dry days, which also serve as an indicator of drought conditions  [20]. This information is of prime importance to the hydrologists, agronomist, hydrogeologists and water-resources managers  [24]  [25]  [26]   [27]. Standardised Precipitation Index (SPI) is used to identify meteorological wet and drought events from precipita-Atmospheric and Climate Sciences tion time series data and serves as a useful tool in water resource management  [28].
Several studies  [1]  [2] [5]  [8]  [21]-  [30]  compared gridded datasets with station measurements, however, no study has been conducted in any part of Nigeria to determine which of the best stable gridded climate products matches well with the observed station datasets, and thus serves as a substitute to the station datasets in this highly data-scarce region. Most previous studies use correlation and error analysis to evaluate the performance of gridded products by comparing them with observed station datasets, but performance assessment of the products based on correlation and error analysis alone can be misleading  [5]. This is because the coefficient of correlation is highly sensitive to outliers, hence may not explain the model capabilities fully  [31]. The RMSE also varies with the variability in squared errors which reduces its ability to determine the degree to which it reflects average errors and to what extent it reflects variability in the distribution of squared errors  [31]   [32].
To overcome these drawbacks, indices like mean bias error (MBE) and spell lengths analysis can be used. MBE is used to analyse the mean in overestimations and underestimations of models. Wet and dry spells analysis are referred to as extended periods of wet and dry days, respectively  [20]  which is used in this study to compare which of the gridded datasets matches the observed datasets in estimating the wet and dry spells. This study aims to: 1) to evaluate data from three gauge based gridded datasets (CRU, PGF and CFSR) daily precipitation, maximum and minimum temperature datasets available for the Niger Delta and compares it with observed station datasets to identify their fundamental differences. 2) analyse different spell characteristics and identify prominent differences in the spells of the datasets and 3) use the Standardised Precipitation Index (SPI) to assess the implications of using the gridded precipitation products on water resource management in Nigeria.
Data The gridded datasets used in this study are daily precipitation, maximum and minimum temperature datasets obtained from CRU, PGF and CFRS for the years 1980 to 2005. The observed station datasets used for this study are located at Warri and Port-Harcourt (PH) international airport, in Delta and Rivers state which were obtained from the Nigerian Meteorological Agency (NIMET). The CRU gridded datasets are extracted from the CRU version 4. 01 global climate dataset  [14]  and downloaded from http://www. cru. uea. ac. uk/. The PGF datasets were developed by the Princeton University Global Meteorological Forcing centre  [18]  and downloaded from http://hydrology. princeton. edu/data/pgf/. While the CFSR datasets were developed by the National Centres for Environ-mental Prediction (NCEP)  [6]  and downloaded from http://cfs.
Methods Two ways are generally used to compare gridded datasets with station observations: 1) Computing the average areal precipitation for each grid box from available station data and comparing them grid-to-grid  [34] ; 2) Interpolating the gridded data to station level and comparing the datasets with the observed station data  [35]. Several methods such as Arithmetic mean, Thiessen polygon, isohyetal method, and gridding or distance weighting are used to estimate areal precipitation from point data. Distance weighting methods are used for gridding of the observed data to the same spatial resolution of the gridded datasets to be compared with the gauges  [36]. For sparse and unevenly distributed gauges, a simple averaging of all the station data within the grid box is preferred to compute the average areal precipitation  [37]   [38].
Previous studies by  [37]  [39]  [40]  [41] reported that for evaluation of gridded data with in situ measurements of only one observed station within a grid box, pairwise statistical analyses between the grid point rainfall estimates and rain gauge estimates are carried out assuming that station rainfall is the average observed rainfall for the grid box. This method has been used by  [35]  [37] [40]  [41]  for the evaluation gridded datasets with observed station measurements. In this study, the performance of the three different gridded precipitation datasets was compared with single-station data located within the grid box. This is because no grid box was found to have more than one observed station. This study is conducted in three steps. The first involves evaluating the performance of gridded datasets using statistical analysis and visual inspection, the second involves the analysis of spells among datasets.
Performance Evaluation of the Datasets The performance of gridded datasets was evaluated statistically by comparing the Seasonal variation plots of the observed and gridded datasets for the whole study period. Performance of the datasets was tested using the coefficient of correlation (R 2 ), the Root mean square error (RMSE) and Mean Bias (MB) and summarised in Table  1  and Table 2. The RMSE measures the global fitness of a predictive model  [42]. In these equations, y and o are observed and simulated datasets, respectively. The correlation coefficient is measuring the degree of dependence between observations and simulated values. As the R value approaches 1, the accuracy of simulated flows is higher. MAE represents the magnitude Atmospheric and Climate Sciences ( ) 1 2 2 1 1 RMSE v N i i i y o N =   = -     ∑ (1) where y and o represent observed and predicted values respectively; and N v is the number of target data used for testing.
Analysis of Spells Characteristics among Datasets The analysis of the characteristics of mean dry and wet spells lengths was conducted for the stations, using CRU, PGF and CFSR precipitation datasets to compare their ability to capture the temporal variations in the spell indices of the observed datasets. These indices are computed for the Niger Delta rainfall for a period of 26 years (1980 to 2005). Previous studies defined a day as a wet day or dry day when the amount of daily precipitation falls above or below 1 mm  [43]. [24] [44] also defined spells as the number of consecutive rainy days with rainfall > 2. 5 mm. A wet day, in general, represents a rainy day while dry day represents a non-rainy day. Wet and dry spells are defined as extended periods of wet and dry days, respectively  [20].
Standardised Precipitation Index (SPI) In this study, SPI was used as a water resource management tool to investigate extreme events in the selected stations. This gives an insight into the implication of the wrong choice of datasets for water resource management. SPI was developed for monitoring and defining meteorological drought and wet events from precipitation time series data  [28]. SPI computes the precipitation deficit for multiple time steps and therefore facilitates the temporal analysis of drought  [46]. It has also been reported that SPI provides a better spatial standardisation than any other indices. Positive SPI values show higher than median precipitation, while negative values indicate less than median precipitation  [47]. SPI is calculated taking the difference of the monthly precipitation (x i ) from the monthly mean ( x ) then dividing by the standard deviation ( σ )  [48].
Performance of Gridded Datasets The graphs comparing the distribution of the monthly mean for the station, descriptive statistics describing the characteristics of the datasets are also summarised in Table  1 , and the statistical indicators are summarised in Table  2 , respectively. These results and figures show that the CRU and PGF datasets performed very well in depicting the study areas datasets. The Mean, Maximum, median and Variance for CRU datasets are very similar to that of the station datasets. The PGF datasets depicted the mean, maximum and variance very well in comparison with the observed datasets but highly underestimated the median values. The mean precipitation values in the CFRS datasets tend to be a bit higher while the variance, maximum and median tend to be lower in comparison with the observed precipitation.
Statistical indicators in Table  2 , also suggest that the RMSE values are found to be lower in CRU datasets for precipitation and minimum temperature in both stations. The R 2 values of precipitation for both CRU and PGF are found to be closer to 1, except for the maximum and minimum temperature where the CFRS shows a better performance with an R 2 value of 0. 79 and 0. 60 in Port-Harcourt station, while the CRU datasets performed better at the Warri station with an R 2 value 0. 9 and 0. 68 in respectively. However, in all cases the datasets demonstrated satisfactory statistical performance. Figure  2  shows the relationship between the observed station, CRU, PGF and CFRS datasets. (Table  1  & Table  2 ), and the graphical visual comparison, there is a better agreement between the monthly means of the observed station datasets and the CRU datasets which shows a better performance to that of PGF and CFRS datasets for both precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) (Figure  2 ) even with the better performance of some of the datasets in correlation and error analysis (Table  2 ).
The Implication for Water Resource Management The Standardised precipitation index for 1-year time step is calculated for the period 1980-2005 for all the four datasets (Station, CRU, PGF and CFSR). Result of the comparison of computed SPI for all the datasets is shown in Figure  5. on  [28]  classification. The CFRS also estimated the near-normal events in the years 1982, 1986 and 1992 to moderate wet events and the very wet event in 1987 to an extremely wet event based on  [28]  classification. In 1991, the extreme wet event that was estimated by the Station, CRU and PGF datasets was also grossly underestimated by the CFRS to a near-normal event. The evaluation and validation of different gridded datasets is necessary for any region in order to determine the best performing datasets in comparison Atmospheric and Climate Sciences with the real station datasets of that region before using any of the datasets for any water resource management analysis.
Discussions Several statistical techniques have been used in this study to assess the performance of three available daily gridded datasets, commonly used in the Niger Delta part of Nigeria. The study suggests that the CRU datasets replicated the observed station datasets more appropriately when different characteristics of the datasets are considered together. In Nigeria, no study has so far been conducted to assess the performance of daily gridded precipitation and temperature data products that can serve as an alternative to the unavailable station datasets. In West Africa, the only study conducted so far was in Burkina-Faso by Mahe et al. ,  [22]. The study evaluated the ability of CRU and SIEREM (Hydro Sciences Montpellier, France) data to reproduce observed station precipitation characteristics of Burkina-Faso and to analyse the consequences of choosing each one of them on the simulated river flows in the five basins across the country.
Mahe et al. ,  [22]  reported the superiority of the SIEREM precipitation in replicating the station observed precipitation product compared to CRU precipitation. AS SIEREM datasets are developed for largely Francophone African countries  [49] , hence they are therefore not available for Nigeria. However, comparison of the results from CRU, SIEREM and Burkina-Faso of meteorological station datasets shows that the distribution of annual rainfalls, Mean, standard deviation and minimum values are very similar  [22]. Several factors contribute to the performance of gridded data products in a particular area which includes the topography of the area, method of interpolation, the quality of observed stations data, numbers of meteorological stations data and the distribution of the observed meteorological stations used in developing the datasets  [50] [51].
The availability of quality long-term raw data for use in the construction of the gridded product is very crucial which is in many cases not good enough especially in developing countries where the data are often found to be nonuniform  [52]. Observed meteorological time-series datasets are generally included in gauge-based gridded database preparation after checking its quality  [5]. These quality control processes differ for different gridded products. In CRU, all the datasets are passed through a two-stage extensive quality control measures conducted manually and using a semi-automatic approach. The data is checked for the consistency in the first stage followed by the removal of stations or months that gives a large error during the interpolation process in the second stage  [53] [54].
CRU also considers essential factors such as elevation during interpolation which consequently, enhances its ability for accurate estimation  [54]. The CRU data was developed from more than 4000 weather stations distributed around the world  [14] [54]. In the southern part of Nigeria alone, more than fifteen station datasets were used as contributing stations which Though PGF was also found to perform very well compared to CFSR in term of various characteristics. It was, however, found to underestimate the mean and median of precipitation, minimum and maximum temperature. This may cause uncertainty in the impact estimated using gridded datasets. It should be noted that no gridded dataset can correctly represent the actual station datasets  [52]. Relevant dataset for a particular study can be selected based on the ability of the dataset to reconstruct specific property of the data required for such study.
Conclusion The performances of three gridded daily precipitation and temperature products, namely, CRU, PGF and CFRS available for the study area and widely used as an alternative to observed precipitation and temperature, were evaluated in this paper. The performance of the data was evaluated using statistical indicators, comparison of time-series graphs and spells lengths analysis among datasets. The Standardised Precipitation Index (SPI) was used to determine the implication of using poor gridded precipitation data for water resource manage-.
a) and Figure 2(b) shows that the mean monthly distribution of CRU and PGF precipitation matches well with the observed station precipitation. The CFRS datasets consistently overestimate the study areas precipitation from September all through to June and underestimate the mean monthly precipitation from during the peak of the rainy season from June to August. In Figure 2(c) and Figure 2(d), a significant difference exists between the observed station maximum temperature and that of the CFRS in January and February and lower temperatures on the CFRS from March all through to November. In Figure 2(e) and Figure 2(f), the CFSR consistently underestimated the station minimum temperature while the PGF consistently overestimated the station minimum temperature. Therefore, as shown by statistical performance.
ment. RMSE and MBE indicate the consistency of the CRU dataset to provide lowest errors in predicted precipitation, and PGF dataset in prediction of maximum and minimum temperatures. Results of spell analysis also show a good association between the station datasets, CRU and PGF for estimating the mean monthly distribution of Wet and Dry Spell lengths. SPI values indicate a good association between observed and CRU and PGF precipitation while the CFRS overestimate and underestimate the SPI in many years. The results revealed a clear superiority of CRU daily precipitation and temperature predictions over the other gridded products for replicating the mean monthly distribution of the station precipitation, maximum and minimum temperature in the Nigeria study area. It can, therefore, be concluded that the CRU datasets are the best performing datasets in this region and hence can confidently be used as observed datasets for further hydrological studies and future climate projection in the study area.
Datasets We created four different datasets that allowed us to consider different outbreak investigation strategies: (A) Clinic-based case identification This dataset consisted of all suspect cases that reported that they visited a formal healthcare setting (defined as government or non-government primary healthcare center/clinic/hospital) following the onset of symptoms. (B) Community-based case identification This dataset consisted of all suspect cases, irrespective of their healthcare seeking behaviors. (C) Community-based household investigation (case households only) This dataset consisted of all suspect cases plus controls consisting of household members of these cases. (D) Community-based household investigation (all households) This dataset consisted of all members of all households in the community, regardless of symptoms.
Data analysis The epidemic curve was constructed using symptom onset date of chikungunya cases. GPS locations of households with and without chikungunya cases were used to prepare spatial distribution maps. For the case-only datasets (datasets A and B), we compared the age and sex distribution of the cases with that for the district from the 2011 census (Bangladesh Bureau of  Statistics, 2011). For the datasets with information on individuals who escaped from infection (datasets C and D), we initially used simple logistic regression to compare the demographics, typical apparel worn, travel history within the last six months, and household characteristics of cases with non-cases. We then built multivariable logistic regression models to identify adjusted risk factors for chikungunya fever. We initially placed all variables with a p-value of <0.
Inferences from community-based case detection An additional 269 suspect cases were identified in the community who did not seek care in formal healthcare facilities. Of these, 246 individuals visited a local pharmacy and 21 individuals visited the informal sector (unlicensed medical practitioner, traditional healer, and homeopath). The distribution of dates of symptom onset for all cases was nearly identical to the distribution for those that visited clinics (Spearman correlation of 0. 95) (Fig. 1A ). The proportion of suspect cases visiting a clinic varied between 18% in 51-55 years age group and 44% in 5 years age group (Fig. 1B ). The conclusions about age, sex, educational levels, use of mosquito controls and clinical presentation of suspect cases were similar when using datasets of all cases or only those that sought care in clinics (Table  1 ), however, those who presented to clinics were more likely to travel outside the district (41% vs 28%, p-value 0.
Inference from community cases plus controls from all community households Incorporating data from the entire community showed that the chikungunya outbreak was largely constrained to the center of the village, with few households affected on the east and west borders but virtually all households affected in the center (Fig. 2C ). This is despite the entire community only being a few hundred meters wide. The expanded dataset also allowed us to understand the risk factors for infection in the wider community. As with the previous analyses, females had an increased risk of being a case (OR: 1. 62, 95% CI 1. 29-2. 03) (Table  2 ), although the difference by sex was concentrated in adults with no difference among children (Fig. 1C ). Further individuals who reported usually wearing clothing that exposed both limbs had 1.
Controls from the wider community were also required to demonstrate which householdlevel characteristics were important for risk, showing that the use of mosquito coils was not protective, and to map spatial heterogeneity in risk, key to intervention development and deployment. This study highlights the significant heterogeneity in healthcare seeking. Even in a small community such as this, cases visited nine different sources of healthcare, three of which could be considered formal healthcare settings. Infectious disease surveillance activities are unlikely to be able to collate datasets from this diverse range of healthcare sources, even among only those within formal sector, suggesting that outbreak investigations that rely on cases that seek healthcare likely substantially underestimate the magnitude of outbreaks.
Our findings highlight how this additional data collection effort can help reveal the drivers of transmission, allowing mechanistic insight into pathogen spread and maximizing opportunities to control, many of which would not be possible from case-based investigations (Table  3 ). Where it is collected, an additional major benefit of the comprehensive dataset is that it can inform mathematical models that reconstruct entire outbreaks, allowing us to estimate the mean transmission distance (previously estimated here at 95 meters)  (Salje et al. , 2016b). Travelling outside Tangail district within the six months before the outbreak was associated with increased chikungunya fever risk. Human movement can introduce CHIKV into new areas, causing epidemics  (Chretien and Linthicum, 2007). No other areas of Bangladesh were reporting outbreaks of CHIKV at this time, though outbreaks may have been missed due to poor surveillance.
c FT-IR measurements in 4-day-old etiolated hypocotyls show alterations in cell wall composition in SYAC1-HAox lines. d The amount of galacturonic acid extracted from 4-day-old etiolated hypocotyls (n = 4-6, average ± SE). Wild type I represents respective control to SYAC-GFPox and GFP-SYAC1ox. Wild type II was isolated from syac1-5 heterozygote population. e The apparent Young modules measured by atomic force microscopy (AFM) in 4-day-old etiolated hypocotyls of wild type and SYAC1-GFPox. In the boxplots, center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by Origin software; whiskers extend 1. 5 times the interquartile range from the 25th and 75th percentiles, individual data points are represented by dots, n = 10-14. Significant differences are indicated as *P < 0.
By contrast, all SYAC1 overexpressors showed more roots in class 4 when compared with the wild type at the lowest spore concentration, thus indicating hypersensitivity to the pathogen. The In contrast, the overexpression of SYAC1ox results in oversensitivity to the pathogen. Five-scale classification was used to evaluate disease severity: 0 (no symptoms), 1 (very small galls mainly on lateral roots and that do not impair the main root), 2 (small galls covering the main root and few lateral roots), 3 (medium to large galls, also including the main root; plant growth might be impaired), and 4 (severe galls on lateral root, main root, or rosette; fine roots completely destroyed; plant growth is reduced). Inoculation was performed with 10 4 , 10 5 , and 10 6 spore concentration. Significant differences between datasets are indicated by different letters.