Analysis Of Different Data Sets Research Articles

Analysis of Different Data Sets of the Same Clinical Trial May Yield Different Results

In a randomized clinical trial (RCT), analysis of different data sets of the same clinical trial may change the meaningfulness of the difference between the study groups depending on which data sets are compared. Possible data sets are intention to treat (ITT), per protocol or on treatment (PP), as treated (AT). The specific conditions that may cause the greatest likelihood of affecting the outcome, depending on the choice of data sets compared should be further investigated.

Combining two national-scale datasets to map soil properties, the case of available magnesium in England and Wales.

SummaryGiven the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant‐available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross‐validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties.Highlights Differences in sample support mean that two datasets on a soil property cannot be combined simply.We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets.The same model allows soil properties to be mapped jointly from such data.This offers a general basis for combining soil datasets from diverse sources

Shear velocity structural characterization around the Lonar crater using joint inversion of ambient noise HVSR and Rayleigh wave dispersion

The Lonar crater in India is the only known hyper-velocity impact crater formed in basalt target on the Earth formed by an extra-terrestrial meteorite impact, with a distinct shock-induced metamorphosed signature. To understand the effects of impact deformation, fracturing, emplacement of the shock-metamorphosed ejecta material and sedimentation structure in the target basalt around the crater, we investigate the sub-surface shear velocity structure around the Lonar crater region. We use three components, 24-hour records of ambient noise (or microtremor) H/V spectral ratio (HVSR) and Rayleigh wave dispersion data through cross-correlation of ambient noise, obtained from 20 broadband seismic stations around the Lonar crater. The HVSR pattern across the Lonar crater does not present common frequency peaks, suggesting a laterally varying lithological arrangement around the crater. We observe that the HVSR curves at the stations show one or more peaks, below 1 Hz and above 2 Hz. To obtain the average sub-surface shear velocity models, we use the joint inversion of the phase dispersion curves of fundamental and higher mode Rayleigh wave with HVSR data sets, using neighborhood algorithm, in the frequency range 0.2–20 Hz and explored up to 750 m depth. As a reliability-check for the shear velocity models, we compute the theoretical HVSR by forward modeling. The theoretical and observed HVSR peaks match fairly well at each broadband station spread across the Lonar crater. The results show the existence of the fractured/weathered and sedimentary layers and also of the fundamental and higher modes of Rayleigh waves altering the shape of the HVSR and the Rayleigh wave dispersion curves. A pattern of the multiple layers in the sediment–package is visible in the shallow and deeper parts up to 600 m depths across the Lonar crater, but with spatial variations, which may be due to variations in the extent of the fractured zone. This spatial asymmetry of shear velocity profiles might also be the effect of an obliquity impact at the Lonar crater. The significance of such a joint analysis of different datasets of surface waves and HVSR is intended to reduce non-uniqueness involved in the non-linear inversions and present more acceptable sub-surface models to enhance the near-surface geophysical understanding of the impacted structure around the Lonar Crater.

Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic Mapper (TM), ALOS PALSAR L-band data, and their combinations) and modeling algorithms (e.g., artificial neural network (ANN), support vector regression (SVR), Random Forest (RF), k-nearest neighbor (kNN), and linear regression (LR)) for AGB estimation in a subtropical region under non-stratification and stratification of forest types. The results show the following: (1) Landsat TM imagery provides more accurate AGB estimates (root mean squared error (RMSE) values in 27.7–29.3 Mg/ha) than ALOS PALSAR (RMSE values in 30.3–33.7 Mg/ha). The combination of TM and PALSAR data has similar performance for ANN and SVR, worse performance for RF and KNN, and slightly improved performance for LR. (2) Overestimation for small AGB values and underestimation for large AGB values are major problems when using the optical (e.g., Landsat) or radar (e.g., ALOS PALSAR) data. (3) LR is still an important tool for AGB modeling, especially for the AGB range of 40–120 Mg/ha. Machine learning algorithms have limited effects on improving AGB estimation overall, but ANN can improve AGB modeling when AGB values are greater than 120 Mg/ha. (4) Forest type and AGB range are important factors that influence AGB modeling performance. (5) Stratification based on forest types improved AGB estimation, especially when AGB was greater than 160 Mg/ha, using the LR approach. This research provides new insight for remote sensing-based AGB modeling for the subtropical forest ecosystem through a comprehensive analysis of different source data, modeling algorithms, and forest types. It is critical to develop an optimal AGB modeling procedure, including the collection of a sufficient number of sample plots, extraction of suitable variables and modeling algorithms, and evaluation of the AGB estimates.

ANALYSIS OF DIFFERENT DATASETS FOR IONIZATION POTENTIAL AND ELECTRON AFFINITY OF QUINONES ON THE BASIS OF DFT CALCULATIONS

The idea of this research deals with theoretical problems of studying quinones as representatives of natural and synthetic compounds with a huge variety of applications in chemical catalysis, biomedical and technical sciences. For this purpose computational chemistry is chosen as an advanced tool for evaluating electronic energies in gase phase for a series of simple quinones, naphthoquinones and anthraquinones as parent compounds for more complicated ones. Ionization potentials and electron affinities (IPs and EAs) of 88 quinones are calculated by the use of B3LYP level of density functional theory (DFT) with different basis sets, and on this base the validation of several databases for electronic energies of quinones is done. The databases for EAs include published results of measurements of absolute electron affinities of pi charge transfer complex acceptors and those determined as relative values from electron transfer equilibrium studies. Non-linear quadratic correlation was found for relationship between calculated and experiment values of electron affinities. Analysis of the relative deviation between obtained calculated results and the three experimental databases indicated the high quality of the database proposed by Hilal et al. based on electron transfer equilibrium studies. The results found in the research are applicable for validation of computational methods and experimental data for electronic energies of quinones.Forcitation:Li Sun, Jianhua Ran, Cong Zhang, Telegin F.Yu. Analysis of different datasets for ionization potential and electron affinity of quinones on the basis of DFT calculations. Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol. 2017. V. 60. N 8. P. 4-12.

Persistence of historical population structure in an endangered species despite near-complete biome conversion in California's San Joaquin Desert.

Genomic responses to habitat conversion can be rapid, providing wildlife managers with time-limited opportunities to enact recovery efforts that use population connectivity information that reflects predisturbance landscapes. Despite near-complete biome conversion, such opportunities may still exist for the endemic fauna and flora of California's San Joaquin Desert, but comprehensive genetic data sets are lacking for nearly all species in the region. To fill this knowledge gap, we studied the rangewide population structure of the endangered blunt-nosed leopard lizard Gambelia sila, a San Joaquin Desert endemic, using restriction site-associated DNA (RAD), microsatellite and mtDNA data to test whether admixture patterns and estimates of effective migration surfaces (EEMS) can identify land areas with high population connectivity prior to the conversion of native xeric habitats. Clustering and phylogenetic analyses indicate a recent shared history between numerous isolated populations and EEMS reveals latent signals of corridors and barriers to gene flow over areas now replaced by agriculture and urbanization. Conflicting histories between the mtDNA and nuclear genomes are consistent with hybridization with the sister species G.wislizenii, raising important questions about where legal protection should end at the southern range limit of G.sila. Comparative analysis of different data sets also adds to a growing list of advantages in using RAD loci for genetic studies of rare species. We demonstrate how the results of this work can serve as an evolutionary guidance tool for managing endemic, arid-adapted taxa in one of the world's most compromised landscapes.

Practical Guidelines for Studies on Sandfly-Borne Phleboviruses: Part I: Important Points to Consider Ante Field Work.

The purpose of this review is to provide practical information to help researchers intending to perform "from field to laboratory" studies on phleboviruses transmitted by sandflies. This guideline addresses the different steps to be considered starting from the field collection of sandflies to the laboratory techniques aiming at the detection, isolation, and characterization of sandfly-borne phleboviruses. In this guideline article, we address the impact of various types of data for an optimal organization of the field work intending to collect wildlife sandflies for subsequent virology studies. Analysis of different data sets should result in the geographic positioning of the trapping stations. The overall planning, the equipment and tools needed, the manpower to be deployed, and the logistics to be anticipated and set up should be organized according to the objectives of the field study for optimal efficiency.

Emerging Use of Spatial Technologies in Natural Resource Management Services

Spatial technology is a vital technology and opens up new opportunities for sustainable use of natural resources, principally land, water, bio-diversity and helps in increasing productivity in perpetuity without ecological harm. Now a day's spatial technology became an important tool for sustainable agriculture that includes environmentally sustainable, socially sustainable and economically viable. Satellite based imagery are used widely in monitoring agriculture, radar imagery are used during monsoon season. Integrated use of geospatial tools with crop models and ground truthing enables timely crop production forecasts and drought assessment and disaster management. Analysis of spatial, temporal and spectral characteristics of concern objects provide valuable information about the biomass and yield estimation, irrigation infrastructure monitoring & performance assessment, reservoir capacity &loss assessment, vegetation vigor, drought stress monitoring, assessment of crop phenological development, crop acreage estimation, cropland mapping, land-use/land-cover (present and past), and also addressing five natural disasters viz., forest fire, flood, earthquake, cyclone, and landslide. Geographical Information System with decision support tools enable combined analysis of different datasets, their visualization, monitoring and management in a timely and most cost-effective manner.

The effect of magmatic intrusions on coalbed methane reservoir characteristics: A case study from the Hoskissons coalbed, Gunnedah Basin, Australia

Magmatic intrusions can deteriorate coalbed methane reservoir quality by precipitating minerals in natural fracture and cleat system. To date, the effect of magmatic intrusions on coal rank and maturity has been studied extensively. However, their impact on fluid flow capacity and gas content is poorly investigated. This study evaluates the impact of magmatic intrusions on reservoir characteristics of the Hoskissons coal interval in the Gunnedah Basin (eastern Australia) where numerous coal-intrusion associations exist. Drill stem test (DST), borehole image logs and core data are used to determine fluid flow characteristics, gas content and quality in 14 wells across the Gunnedah Basin. The integration of borehole image logs and DST data analysis enables us to determine the existence, openness, and hydraulic conductivity of natural fracture and cleat systems in the Hoskissons coal interval. In addition, available desorption canister data, gas composition data, and conventional well logs are interpreted to investigate probable thermal effect on gas content.Our analyses of different datasets reveal that the thickness of intrusions and their positions with respect to the Hoskissons coal interval are variable in the studied wells. Permeability varies from 1091mD down to zero owing to heterogeneous fracture and cleat systems. Interpreted natural fracture/cleat systems are well correlated with measured permeability from DST data analysis. This highlights the role of open natural fractures/cleats in fluid flow characteristics of the Hoskissons coal interval. Results indicate that the mineralizing effect of hydrothermal fluids derived either from magmatic intrusions or coal itself is not a controlling factor in fluid flow capacity of the Hoskissons coal interval in the studied wells. This is described by either the distance between coal section and major intrusions in some wells or perhaps emplacement of intrusive bodies prior to development of cleat and natural fracture networks. The destructive impact of intrusions on permeability is observed in one of the studied wells in which in-situ stress perturbation is large (due to presence of magmatic intrusions in sedimentary rocks). Variable in-situ stress orientation can decrease fracture connectivity and consequently fluid flow properties are affected. Gas content largely varies in heat affected coal intervals. This signature is the result of thermal effect fading with distance and is more pronounced when intrusions are in close proximity to coal intervals. Gas composition is variable in the studied wells. Gas composition data indicate that high quality desorbed gas with methane concentration higher than 90% could be found even in coal intervals which are heavily intruded.

Calibrated Bayes Factors in Assessing Genetic Association Models

ABSTRACTThree competing genetic models—additive, dominant, and recessive—are often considered in genetic association analysis. We propose and develop a calibrated Bayes approach for comparing these competing models that has the desired property of giving equal support to the three models when no genetic association is present. The naïve approach with noncalibrated priors is shown to produce misleading Bayes factors. The method is fully developed with simulation studies, real data analyses, and an efficient algorithm based on an asymptotic approximation. An illuminating connection to the Kullback–Leibler divergence is also established. The proposed calibrated prior can serve as a reference prior for a genetic association study or as a common baseline prior for comparing Bayes analyses of different datasets.

MATISSE: A novel tool to access, visualize and analyse data from planetary exploration missions

The increasing number and complexity of planetary exploration space missions require new tools to access, visualize and analyse data to improve their scientific return. ASI Science Data Center (ASDC) addresses this request with the web-tool MATISSE (Multi-purpose Advanced Tool for the Instruments of the Solar System Exploration), allowing the visualization of single observation or real-time computed high-order products, directly projected on the three-dimensional model of the selected target body. Using MATISSE it will be no longer needed to download huge quantity of data or to write down a specific code for every instrument analysed, greatly encouraging studies based on joint analysis of different datasets. In addition the extremely high-resolution output, to be used offline with a Python-based free software, together with the files to be read with specific GIS software, makes it a valuable tool to further process the data at the best spatial accuracy available. MATISSE modular structure permits addition of new missions or tasks and, thanks to dedicated future developments, it would be possible to make it compliant to the Planetary Virtual Observatory standards currently under definition. In this context the recent development of an interface to the NASA ODE REST API by which it is possible to access to public repositories is set.

Statistical analysis of empirical lifetime mileage data for automotive LCA

Innovative fuel efficiency technologies as for example electrification and lightweight materials typically lead to a shift of emissions from the use phase to the production phase of a vehicle. Whether the efforts of applying sophisticated fuel saving technologies pay back in environmental terms over the products’ lifetime depends directly on the lifetime mileage assumed. Currently, different “educated guess” values of industry associations and car manufacturers ranging from 150,000 to 300,000 km are applied in automotive life cycle assessment (LCA). This study improves the database for LCA practitioners in the automotive industry by statistically determining empirical lifetime mileage values for different types of passenger cars. The analysis of different data sets of a sample of more than 800,000 vehicles in Germany is performed by using statistical techniques. Firstly, survival analysis is used to describe the typical lifetime of passenger cars by using the Weibull distribution. Secondly, regression analysis is used to describe the progression of mileage during the course of a car’s life. The analysis differentiates eight different segments from urban small cars (A000) over midsized cars (A00, A0, A, B, C, D) to ultra luxury cars (E) as well as Otto and diesel engines. The results show varying lifetimes and mileages of passenger cars depending on the engine type and vehicle segment. Many decisions on innovative automotive technologies are independent from the engine type. In this case, the recommendation of the authors is to differentiate between three different groups of segments (A00/A0, A/B, C) with mileages going from 170,000 over 200,000 to 230,000 km. There might even be cases where the usage of a segment-specific value is not recommended. In such cases, the average value of the obtained results of 200,000 km appears as feasible choice. Even though the primary data are from Germany only, it is assumed that the results obtained in this study can be used as a proxy estimate for the geographical area of Europe. The results of this paper can be used as default values for LCA practitioners performing automotive LCAs as they provide evidence-based values of passenger cars’ lifetimes and mileages in a disaggregated way. The previously often used assumption of 150,000 km based on educated guess represents an underestimated value and should be replaced by the data obtained in this study. Using the proposed lifetime mileage data improves the validity and representativeness of automotive LCAs in Europe.

Simultaneous Radio and EUV Imaging of a Multi-lane Coronal Type II Radio Burst

A multi-lane solar type II radio burst was observed by several solar spectrographs on 16 February 2011. The event was also recorded by the Nancay Radioheliograph (NRH) at several metric wavelengths, by the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO), and by the Extreme Ultraviolet Imager (EUVI) onboard the Solar TErrestrial Relations Observatory (STEREO) in a number of EUV passbands. These multi-wavelength data provide a rare opportunity to reveal the emission source of the multiple type II lanes. Our study shows that all lanes are associated with a single EUV wave, presumably the radio-emitting shock. The EUV wave was driven by a coronal mass ejection (CME) associated with an M1.6 flare and a filament eruption. With the NRH data and the three-dimensional (3D) bow-shock reconstruction that we built using the multi-viewpoint data of the EUV wave, we are able to deduce the 3D coordinates of the radio sources. We conclude that all the three type II lanes originated from the western flank of the shock, with two of them from closely adjacent locations on the southern part, the other one from a distinct location on the northern part. This case study demonstrates how the type II origin can be pinpointed by combining analyses of different data sets.

Multiple Boosting in the Ant Colony Decision Forest meta-classifier

The idea of ensemble methodology is to combine multiple predictive models in order to achieve a better prediction performance. In this task we analyze the self-adaptive methods for improving the performance of Ant Colony Decision Tree and Forest algorithms. Our goal is to present and compare new meta-ensemble approaches based on Ant Colony Optimization. The proposed meta-classifiers (consisting of homogeneous classifiers) can be characterized by the self-adaptability or the good accommodation with the analyzed data sets and offer appropriate classification accuracy.In this article we provide an overview of ensemble methods in classification tasks and concentrate on the different methodologies, such as Bagging, Boosting and Random Forest. We present all important types of ensemble methods including Boosting and Bagging in context of distributed approach, where agent-ants create better solutions employing adaptive mechanisms. Self adaptive, combining methods and modeling appropriate issues, such as ensembles presented here are discussed in context of the quality of the results. Smaller trees in decision forest without loss of accuracy are achieved during the analysis of different data sets.

The influence of age on interlayer shear properties

The paper focuses on the question of the influence of age on the interlayer bonding properties of two-layered specimens. The evaluation combines traditional data evaluation and artificial neural network (ANN) analysis of different data sets of pavements from Switzerland, United Kingdom and Italy. The results show that it is possible to use ANN techniques to derive models from data sets and to predict interlayer bond strength. The findings demonstrate that age has a positive effect on the interlayer bond of asphalt pavements and that long-term oven ageing can lead to similar results as in situ ageing. In addition, it was found that the positive effect of ageing is greater when a tack coat is used.

The Need for Market Segmentation in Buy-Till-You-Defect Models

Buy-till-you-defect [BTYD] models are built for companies operating in a non- contractual setting to predict customers’ transaction frequency, amount and timing as well as customer lifetime. These models tend to perform well, although they often predict unrealistically long lifetimes for a substantial fraction of the customer base. This obvious lack of face validity limits the adoption of these models by practitioners. Moreover, it highlights a flaw in these models. Based on a simulation study and an empirical analysis of different datasets, we argue that such long lifetime predictions can result from the existence of multiple segments in the customer base. In most cases there are at least two segments: one consisting of customers who purchase the service or product only a few times and the other of those who are frequent purchasers. Customer heterogeneity modeling in the current BTYD models is insufficient to account for such segments, thereby producing unrealistic lifetime predictions. We present an extension over the current BTYD models to address the extreme lifetime prediction issue where we allow for segments within the customer base. More specifically, we consider a mixture of log-normals distribution to capture the heterogeneity across customers. Our model can be seen as a variant of the hierarchical Bayes [HB] Pareto/NBD model. In addition, the proposed model allows us to relate segment membership as well as within segment customer heterogeneity to selected customer characteristics. Our model, therefore, also increases the explanatory power of BTYD models to a great extent. We are now able to evaluate the impact of customers’ characteristics on the membership probabilities of different segments. This allows, for example, one to a-priori predict which customers are likely to become frequent purchasers. The proposed model is compared against the benchmark Pareto/NBD model (Schmittlein, Morrison, and Colombo 1987) and its HB extension (Abe 2009) on simulated datasets as well as on a real dataset from a large grocery e-retailer in a Western European country. Our BTYD model indeed provides a useful customer segmentation that allows managers to draw conclusions on how customers’ purchase and defection behavior are associated with their shopping characteristics such as basket size and the delivery fee paid.

Properties of the first excited state of9Be derived from(γ,n)and(e,e′)reactions

Properties of the first excited state of the nucleus 9Be are discussed based on recent (e,e') and (gamma,n) experiments. The parameters of an R-matrix analysis of different data sets are consistent with a resonance rather than a virtual state predicted by some model calculations. The energy and the width of the resonance are deduced. Their values are rather similar for all data sets, and the energy proves to be negative. It is argued that the disagreement between the extracted B(E1) values may stem from different ways of integration of the resonance. If corrected, fair agreement between the (e,e') and one of the (gamma,n) data sets is found. A recent (gamma,n) experiment at the HIgS facility exhibits larger cross sections close to the neutron threshold which remain to be explained.

Why is it so difficult to identify a single indicator of water stress in plants? A proposal for a multivariate analysis to assess emergent properties

Because of the complexity of plant responses to water deficit, researchers have attempted to identify simplified models to understand critical aspects of the problem by searching for single indicators that would enable evaluations of the effects of environmental changes on the entire plant. However, this reductionist approach, which is often used in plant sciences, makes it difficult to distinguish systemic emergent behaviours. Currently, a new class of models and epistemology have called attention to the fundamental properties of complex systems. These properties, termed 'emergent', are observed at a large scale of the system (top hierarchical level) but cannot be observed or inferred from smaller scales of observation in the same system. We propose that multivariate statistical analysis can provide a suitable tool to quantify global responses to water deficit, allowing a specific and partially quantitative assessment of emergent properties. Based on an experimental study, our results showed that the classical approach of the individual analysis of different data sets might provide different interpretations for the observed effects of water deficit. These results support the hypothesis that a cross-scale multivariate analysis is an appropriate method to establish models for systemic understanding of the interactions between plants and their changing environment.

Gene and Genome Parameters of Mammalian Liver Circadian Genes (LCGs)

The mammalian circadian system controls various physiology processes and behavior responses by regulating thousands of circadian genes with rhythmic expressions. In this study, we redefined circadian-regulated genes based on published results in the mouse liver and compared them with other gene groups defined relative to circadian regulations, especially the non-circadian-regulated genes expressed in liver at multiple molecular levels from gene position to protein expression based on integrative analyses of different datasets from the literature. Based on the intra-tissue analysis, the liver circadian genes or LCGs show unique features when compared to other gene groups. First, LCGs in general have less neighboring genes and larger in both genomic and 3′-UTR lengths but shorter in CDS (coding sequence) lengths. Second, LCGs have higher mRNA and protein abundance, higher temporal expression variations, and shorter mRNA half-life. Third, more than 60% of LCGs form major co-expression clusters centered in four temporal windows: dawn, day, dusk, and night. In addition, larger and smaller LCGs are found mainly expressed in the day and night temporal windows, respectively, and we believe that LCGs are well-partitioned into the gene expression regulatory network that takes advantage of gene size, expression constraint, and chromosomal architecture. Based on inter-tissue analysis, more than half of LCGs are ubiquitously expressed in multiple tissues but only show rhythmical expression in one or limited number of tissues. LCGs show at least three-fold lower expression variations across the temporal windows than those among different tissues, and this observation suggests that temporal expression variations regulated by the circadian system is relatively subtle as compared with the tissue expression variations formed during development. Taken together, we suggest that the circadian system selects gene parameters in a cost effective way to improve tissue-specific functions by adapting temporal variations from the environment over evolutionary time scales.

Monitoring Drought Conditions and Their Uncertainties in Africa Using TRMM Data

AbstractThe main objective of this study is to evaluate the uncertainties due to sample size associated with the estimation of the standardized precipitation index (SPI) and their impact on the level of confidence in drought monitoring in Africa using high-spatial-resolution data from short time series. To do this, two different rainfall datasets, each available on a monthly basis, were analyzed over four river basins in Africa—Oum er-Rbia, Limpopo, Niger, and eastern Nile—as well as at the continental level. The two precipitation datasets used were the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product 3B43 and the Global Precipitation Climatology Centre full-reanalysis gridded precipitation dataset. A nonparametric resampling bootstrap approach was used to compute the confidence bands associated with the SPI estimation, which are essential for making a qualified assessment of drought events. The comparative analysis of different datasets suggests that for reliable drought monitoring over Africa it is feasible to use short time series of remote sensing precipitation data, such as those from TRMM, that have a higher spatial resolution than other gridded precipitation data. The proposed approach for drought monitoring has the potential to be used in support of decision making at both continental and subcontinental scales over Africa or over other regions that have a sparse distribution of rainfall measurement instruments.