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Premise: The Flora e Funga do Brasil project is the most comprehensive effort to reliably document Brazilian plant and fungal diversity. It involves the collaborative work of hundreds of taxonomists, integrating detailed and standardized morphological descriptions, nomenclatural status, and geographic distribution information of plants, algae, and fungi collected throughout Brazil. Despite the extensive information available, managing the information from the Flora e Funga do Brasil website poses certain challenges. Methods and Results: florabr is an R package developed to facilitate the exploration and geographical analysis of species information derived from the Flora e Funga do Brasil. Unique to florabr is its ability to interact with the latest, or any other version of the dataset, which undergoes weekly updates. I illustrate the practical application of florabr in common tasks in biogeography and conservation studies. Conclusions: florabr is anticipated to be of significant interest to biogeographers, ecologists, curators of biological collections, and taxonomists actively contributing to the Flora e Funga do Brasil. [ABSTRACT FROM AUTHOR]

Image preprocessing is a method to transform raw image data into clean image data. The objective of preprocessing is to improve the image data by suppressing undesired distortions. Enhancement of some image features which are relevant for further processing of image and analysis task is also done in preprocessing. Screening and diagnosis of various eye diseases like diabetic retinopathy, Choroidal Neovascularization(CNV), DRUSEN, etc. are possible using digital retinal images. This paper aims to provide a better understanding and knowledge of the computer algorithms used for retinal image preprocessing. In this paper, various image preprocessing techniques are incorporated such as color correction, color space selection, noise reduction, and contrast enhancement on retinal images. Retinal blood vessels are better seen in Green color space instead of Red or Blue color space. Noise reduction through Block matching and 3D(BM3D) techniques show a significant result as compared to Total Variation Filter (TVF) and Bilateral Filter (BLF). Contrast enhancement through Contrast Limited Adaptive Histogram Equalization (CLAHE) outperforms Global Equalization (GE) or Adaptive Histogram Equalization (AHE). Evaluation parameters such as Mean square error, Peak Signal Noise ratio, Structured similarity index measures, and Normalized root mean square error values for BM3D noise filtering are 0.0029, 25.3370, 0.6839 and 0.0998 respectively which shows that BM3D outperforms the others. [ABSTRACT FROM AUTHOR]

Aim: Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation: We introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions: occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application. [ABSTRACT FROM AUTHOR]

The precise prediction of NOx generation concentration in coal‐fired boilers serves as the foundational cornerstone for the judicious optimization and control of selective catalytic reduction denitrification (SCR) systems. Owing to the intricate nature of the denitrification process within SCR, there exists a temporal delay in regulating the ammonia injection rate based on the monitored data of NOx concentration at the SCR inlet. Such delays can give rise to ammonia leakage and subsequent obstruction of the air preheater. In light of this, a predictive model, CEEMDAN‐LSTM‐SA, is proposed as an amalgamation of data decomposition and the LSTM (long short‐term memory) fusion self‐attention mechanism within a deep learning network, which is introduced to forecast the NOx emission concentration at the SCR inlet of coal‐fired units. To mitigate the impact of data outliers on the training effectiveness of the model, a clustering method coupled with a statistical testing strategy is initially applied to refine the dataset first. CEEMDAN data decomposition technology is leveraged to facilitate the breakdown of data, alleviating its non‐stationary and intricate characteristics. Subsequently, through spectral analysis, the decomposed components are grouped and aggregated to form novel data elements, which are then subjected to prediction by the constructed LSTM‐SA deep learning network. The ultimate NOx emission concentration prediction value is derived through a process of fusion. Upon scrutinizing and comparing the predictions derived from various models using coal‐fired power plant data, it is evident that the performance metrics of CEEMDAN‐LSTM‐SA predictions exhibit a mean absolute error of 7.425, mean absolute percentage error of 2.415%, root mean square error of 9.715, R‐squared (R2) value of.789, mean absolute relative error of 2.109%, and a Theil's information criterion of.016. In contrast to other models, including traditional self‐attention networks, LSTM, and LSTM‐SA combination networks, CEEMDAN‐LSTM‐SA proposed in this study demonstrates superior prediction accuracy and enhanced generalization capabilities. Consequently, this predictive model stands poised to furnish an efficacious framework for the SCR ammonia injection strategy within thermal power units. [ABSTRACT FROM AUTHOR]

Plant trait data are used to quantify how plants respond to environmental factors and can act as indicators of ecosystem function. Measured trait values are influenced by genetics, trade‐offs, competition, environmental conditions, and phenology. These interacting effects on traits are poorly characterized across taxa, and for many traits, measurement protocols are not standardized. As a result, ancillary information about growth and measurement conditions can be highly variable, requiring a flexible data structure. In 2007, the TRY initiative was founded as an integrated database of plant trait data, including ancillary attributes relevant to understanding and interpreting the trait values. The TRY database now integrates around 700 original and collective datasets and has become a central resource of plant trait data. These data are provided in a generic long‐table format, where a unique identifier links different trait records and ancillary data measured on the same entity. Due to the high number of trait records, plant taxa, and types of traits and ancillary data released from the TRY database, data preprocessing is necessary but not straightforward. Here, we present the 'rtry' R package, specifically designed to support plant trait data exploration and filtering. By integrating a subset of existing R functions essential for preprocessing, 'rtry' avoids the need for users to navigate the extensive R ecosystem and provides the functions under a consistent syntax. 'rtry' is therefore easy to use even for beginners in R. Notably, 'rtry' does not support data retrieval or analysis; rather, it focuses on the preprocessing tasks to optimize data quality. While 'rtry' primarily targets TRY data, its utility extends to data from other sources, such as the National Ecological Observatory Network (NEON). The 'rtry' package is available on the Comprehensive R Archive Network (CRAN; https://cran.r‐project.org/package=rtry) and the GitHub Wiki (https://github.com/MPI‐BGC‐Functional‐Biogeography/rtry/wiki) along with comprehensive documentation and vignettes describing detailed data preprocessing workflows. [ABSTRACT FROM AUTHOR]

Ecology increasingly relies on a massive volume of biodiversity occurrence records to draw insights into large‐scale biogeographical, ecological and evolutionary phenomena. This often involves defining a set of criteria that guides the collection, filtering and standardising of available records. These curation processes are often neither described in detail nor well documented. This hampers the comparability and reproducibility of studies and thus undermines the robustness of any ecological result. Yet, to date, there is no guide providing a friendly way to reason and document a complete data curation process.We reviewed the available literature on data curation, including tools such as R packages and workflows. From this assessment, we created a complete guide organised into five modules that allows users to consciously select and validate occurrence records based on these curation criteria. This is presented in the user‐friendly Shiny R OCCUR application, available at https://ecoinformatic.shinyapps.io/OCCUR/.OCCUR application provides a guide for researchers to deal with the trade‐off between data certainty and coverage generated in each data curation step. An interactive graph of these changes is provided within each module. OCCUR also produces a custom‐made final report including all steps used for data filtering. This report helps to streamline the writing of the methods section of manuscripts and technical reports, thus promoting the reproducibility of data curation processes.This Shiny application provides an interactive overview of the data curation methods and their use for handling occurrence records from public repositories. It brings together the taxonomic, temporal and spatial dimensions of data and also the identification of duplicates of the records. OCCUR can be applied for multiple purposes, such as teaching R to ecologists or enhancing reproducibility of macroecology and biogeography. [ABSTRACT FROM AUTHOR]

Introduction/Purpose: Ultrasound picture archiving and communication system (PACS) databases are useful for quality improvement and clinical research but frequently contain free text that is not easily readable. Here, we present a method to extract and clean a semi‐structured echocardiography (cardiac ultrasound) PACS database. Methods: Echocardiography studies between 1 January 2010 and 31 December 2018 were extracted using a data mining tool. Numeric variables were recoded with extreme values excluded. Analysis of free text, including descriptions of the heart valves and right and left ventricular size and function, was performed using a rule‐based system. Different levels of free text variables were initially identified using commonly used phrases and then iteratively developed. Randomly selected sets of 100 studies were compared to the electronic health record to validate the data cleaning process. Results: The data validation step was performed three times in total, with Cohen's kappa ranging between 0.88 and 1.00 for the final set of data validation across all measures. Conclusion: Free text cleaning of semi‐structured PACS databases is possible using freely available open‐source software. The accuracy of this method is high, and the resulting dataset can be linked to administrative data to answer research questions. We present a method that could be used to answer clinical questions or to develop quality improvement initiatives. [ABSTRACT FROM AUTHOR]

In this paper, the historical network loss is calculated by using the cleaned historical data collected by the user electricity meter, the transformer gateway meter every 15 min and the gateway meter of the transformer substation every half an hour. Use machine learning technologies to analyze the loss of distribution network, reveal the changing law of distribution network loss and its influencing factors (electrical/non‐electrical parameters), and predict future distribution network loss. Pearson correlation, characteristics of the Random Forest important attributes and MIC (Maximal Information Coefficient) are adopted to analyze correlations between the power network loss and the electrical parameters such as unbalanced three‐phase, power factor, load rate, active power, reactive power together with atmospheric factors (temperature, humidity, and wind speed). The factors strongly related to network loss as the input of the multi‐parallel input multi‐step output stacking LSTM (Long Term Memory) prediction model to predict network loss. The simulation data in this paper comes from the actual data of 44 subareas of the 10 KV distribution network. The experimental results show that the data cleaning methods: the cluster‐based Local Outlier Factor algorithm and the improved Random Forest imputation algorithm are effective for enhancing the prediction accuracy. Especially the improved Random Forest algorithm is robust to all missing forms. Compared with the baseline model, the multi‐parallel input multi‐step output stacking LSTM prediction model has higher forecasting accuracy. [ABSTRACT FROM AUTHOR]

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Technical artifacts such as clogging that occur during the data acquisition process of flow cytometry data can cause spurious events and fluorescence intensity shifting that impact the quality of the data and its analysis results. These events should be identified and potentially removed before being passed to the next stage of analysis. flowCut, an R package, automatically detects anomaly events in flow cytometry experiments and flags files for potential review. Its results are on par with manual analysis and it outperforms existing automated approaches. [ABSTRACT FROM AUTHOR]

In daily monitoring of structures instrumented with long‐term structural health monitoring (SHM) systems, the acquired data is often corrupted with gross outliers due to hardware imperfection and/or electromagnetic interference. These unexpected spikes in data are not unusual and their existence may greatly influence the results of structural health evaluation and lead to false alarms. Hence, there is a high demand for executing data cleaning and data recovery, especially in harsh monitoring environment. In this paper, we propose a robust gross outlier removal method, termed Hankel‐structured robust principal component analysis (HRPCA), to remove gross outliers in the monitoring data of structural dynamic responses. Different from the deep‐learning‐based approaches that possess only outlier identification or anomaly classification ability, HRPCA is a rapid and integrated methodology for data cleaning, which enables outlier detection, outlier identification, and recovery of fault data. It capitalizes on the fundamental duality between the sparsity of the signal and the rank of the structured matrix. Using annihilating filter‐based fundamental duality, structural responses could be modeled as lying in a low‐dimensional subspace with additional Hankel structure; thus, the gross outliers could be represented as a sparse component. Then the outlier removal issue turns into a matrix factorization problem, which could be successfully solved by robust principal component analysis (RPCA). To validate the denoising capability of HRPCA, a laboratory experiment is first conducted on a five‐story building model where the reference clean signal is aware. Then real‐world monitoring data with varying degrees of outliers (e.g., single outlier, multiple outliers, and periodic outliers) collected from a cable‐stayed bridge and a high‐rise structure is used to further illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Fossil occurrence databases are indispensable resources to the palaeontological community, yet present unique data cleaning challenges. Many studies devote significant attention to cleaning fossil occurrence data prior to analysis, but such efforts are typically bespoke and difficult to reproduce. There are also no standardised methods to detect and resolve errors despite the development of an ecosystem of cleaning tools fuelled by the concurrent growth of neontological occurrence databases.As fossil occurrence databases continue to increase in size, the demand for standardised, automated and reproducible methods to improve data quality will only grow. Here, we present semi‐automated cleaning solutions to address these issues with a new R package fossilbrush. We apply our cleaning protocols to the Paleobiology Database to assess the prevalence of anomalous entries and the efficacy and impact of our methods.We find that anomalies may be effectively resolved by comparison against a published compendium of stratigraphic ranges, improving the stratigraphic quality of the data, and through methods which detect outliers in taxon‐wise occurrence stratigraphic distributions. Despite this, anomalous entries remain prevalent throughout major clades, with often more than 30% of genera in major fossil groups (e.g. bivalves, echinoderms) displaying stratigraphically suspect occurrence records.Our methods provide a way to flag and resolve anomalous taxonomic data before downstream palaeobiological analysis and may also aid in the automation and targeting of future cleaning efforts. We stress, however, that our methods are semi‐automated and are primarily for the detection of potential anomalies for further scrutiny, as full automation should not be a substitute for expert vetting. We note that some of our methods do not rely on external databases for anomaly resolution and so are also applicable to occurrences in neontological databases, expanding the utility of the fossilbrush R package. [ABSTRACT FROM AUTHOR]

Premise: There has been a great increase in using climatic data in phylogenetic studies over the past decades. However, compiling the high‐quality spatial data needed to perform accurate climatic reconstructions is time‐consuming and can result in poor geographical coverage. Therefore, researchers often resort to qualitative approximations. Our aim was to evaluate the climatic characterization of the genera of the Asian Palmate Group (AsPG) of Araliaceae as an exemplar lineage of plants showing tropical–temperate transitions. Methods: We compiled a curated worldwide spatial database of the AsPG genera and created five raster layers representing bioclimatic regionalizations of the world. Then, we crossed the database with the layers to climatically characterize the AsPG genera. Results: We found large disagreement in the climatic characterization of genera among regionalizations and little support for the climatic nature of the tropical–temperate distribution of the AsPG. Both results are attributed to the complexity of delimiting tropical, subtropical, and temperate climates in the world and to the distribution of the study group in regions with transitional climatic conditions. Conclusions: The complexity in the climatic classification of this example of the tropical–temperate transitions calls for a general climatic revision of other tropical–temperate lineages. In fact, we argue that, to properly evaluate tropical–temperate transitions across the tree of life, we cannot ignore the complexity of distribution ranges. [ABSTRACT FROM AUTHOR]

The increase in online and openly accessible biodiversity databases provides a vast and invaluable resource to support research and policy. However, without scrutiny, errors in primary species occurrence data can lead to erroneous results and misleading information.Here, we introduce the Biodiversity Data Cleaning (bdc), an R package to address quality issues and improve the fitness‐for‐use of biodiversity datasets. The bdc package brings together several aspects of biodiversity data cleaning in one place. It is organized in thematic modules related to different biodiversity dimensions, including (a) Merge datasets: standardization and integration of different datasets; (b) Pre‐filter: flagging and removal of invalid or non‐interpretable information, followed by data amendments; (c) Taxonomy: cleaning, parsing and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; (d) Space: flagging of erroneous, suspect and low‐precision geographic coordinates; and (e) Time: flagging and, whenever possible, correction of inconsistent collection date. In addition, the package contains features to visualize, document and report data quality—which is essential for making data quality assessment transparent and reproducible. The modules illustrated, and functions within, were linked to form a proposed reproducible workflow that can also integrate functions from other R packages.We demonstrated the bdc package's applicability in cleaning more than 30 million occurrence records for terrestrial plant species in Brazil. We found that around one‐fifth of the original datasets hold the standard quality requirements.Compared to other available R packages, the main strengths of the bdc package are that it brings together available tools—and a series of new ones—to assess the quality of different dimensions of biodiversity data into a single and flexible toolkit. The functions can be applied to many taxonomic groups, datasets (including regional or local repositories), countries, or world‐wide. We hope the bdc package can facilitate the data cleaning process and catalyse improvements to allow the wise and efficient use of primary biodiversity data. [ABSTRACT FROM AUTHOR]

In this paper, we compare the most popular Atom‐to‐Atom Mapping (AAM) tools: ChemAxon,[1] Indigo,[2] RDTool,[3] NameRXN (NextMove),[4] and RXNMapper[5] which implement different AAM algorithms. An open‐source RDTool program was optimized, and its modified version ("new RDTool") was considered together with several consensus mapping strategies. The Condensed Graph of Reaction approach was used to calculate chemical distances and develop the "AAM fixer" algorithm for an automatized correction of erroneous mapping. The benchmarking calculations were performed on a Golden dataset containing 1851 manually mapped and curated reactions. The best performing RXNMapper program together with the AMM Fixer was applied to map the USPTO database. The Golden dataset, mapped USPTO and optimized RDTool are available in the GitHub repository https://github.com/Laboratoire‐de‐Chemoinformatique. [ABSTRACT FROM AUTHOR]

Modern, high‐throughput animal tracking increasingly yields 'big data' at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis‐estimation of behaviours inferred from movement. 'Cleaning' the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce.A pipeline for cleaning massive high‐throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre‐processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time‐consuming, and to boost reproducibility, pre‐processing pipelines must be automated.We provide guidance on building pipelines for pre‐processing high‐throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful 'residence patches', for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre‐processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre‐processing pipeline and residence patch method in a fully worked out example.To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre‐processing pipeline and atlastools can be used with any high‐throughput animal movement data in which the high data‐volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre‐processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data. [ABSTRACT FROM AUTHOR]

The quality of experimental data for chemical reactions is a critical consideration for any reaction‐driven study. However, the curation of reaction data has not been extensively discussed in the literature so far. Here, we suggest a 4 steps protocol that includes the curation of individual structures (reactants and products), chemical transformations, reaction conditions and endpoints. Its implementation in Python3 using CGRTools toolkit has been used to clean three popular reaction databases Reaxys, USPTO and Pistachio. The curated USPTO database is available in the GitHub repository (Laboratoire‐de‐Chemoinformatique/Reaction_Data_Cleaning). [ABSTRACT FROM AUTHOR]

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre‐processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function‐based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS–IENN‐based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS–IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high‐classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K‐nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree. [ABSTRACT FROM AUTHOR]

Abstract: The increase of digitally available primary biodiversity data has been a positive result of sharing initiatives in the natural history museum community and among citizen scientists. Owing to the heterogeneity of sources, however, limitations related to data quality control emerge, as incomplete and/or erroneous information at different stages of input must be overcome. To facilitate detection of spatial errors, species distribution modelling (SDM) has been suggested, but its efficiency in detection of different types of spatial errors has not been assessed. We investigate the utility of SDM‐based assessments in detection of two types of spatial errors found in large biodiversity databases, random errors versus errors of misidentification as congeneric taxa. We used available distributional data for five closely related species of the tortoise beetle genus Mesomphalia (Coleoptera, Chyrsomelidae, Cassidinae) to test the suitability values associated with simulated erroneous points mimicking the two error types. Overall, we observed that habitat suitability values associated with random outliers were lower than those for congeneric outliers, fitting expectations based on the idea of niche conservatism. Also, detecting outliers in small datasets is more challenging, whereas in larger datasets, the detection of random outliers should be more efficient. Our results indicate that SDM tools can be useful in detection of outliers more efficiently when erroneous points fall outside the ecological niche profile of the species, as in the case of random typographical errors, but not as effective with errors of misidentification. This paper explores a potential tool to promote better assessment of the quality of biodiversity data. [ABSTRACT FROM AUTHOR]

Summary: Misinvoicing is a major tool in fraud including money laundering. We develop a method of detecting the patterns of outliers that indicate systematic mis‐pricing. As the data only become available year by year, we develop a combination of very robust regression and the use of ‘cleaned’ prior information from earlier years, which leads to early and sharp indication of potentially fraudulent activity that can be passed to legal agencies to institute prosecution. As an example, we use yearly imports of a specific seafood into the European Union. This is only one of over one million annual data sets, each of which can currently potentially contain 336 observations. We provide a solution to the resulting big data problem, which requires analysis with the minimum of human intervention. [ABSTRACT FROM AUTHOR]

Big data is a new term used to identify the large scale and complex datasets. Big data is now rapidly expanding in all science and engineering domains, owing to the fast development of networking, data storage, and data collection capacity. Big data mining is the capability of extracting useful information from these large datasets. Nowadays, the integration of cloud computing with the data mining for the big data mining process is a challenging task. In order to process the huge amount of data, it is necessary to concentrate the improvement on the big data computation. Most of the existing approaches use the MapReduce to compute the big data. The increase in the computational cost and memory consumption are the main drawbacks of the existing approaches. To overcome these limitations, this paper proposes a similarity-based clustering and optimum cache replacement approach for big data computing applications. The job recovery process is initiated by copying the data in the cloud server and forwarding the data copy for further processing. Then, the job is divided into clusters based on the similarity-based clustering approach. Finally, the cache concept is introduced with the optimum cache replacement algorithm to avoid repeated execution of the jobs by queue management. The proposed approach is compared with the existing Spark and Hadoop approaches. The proposed approach achieves better performance in terms of iteration time, query response time, job completion time, and clustering accuracy. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Data quality is one of the highest priorities for species distribution data warehouses, as well as one of the main concerns of data users. There is the need, however, for computational procedures with the facility to automatically or semi-automatically identify and correct errors and to seamlessly integrate expert knowledge and automated processes., New version modestr 2.0 () makes it easy to download occurrence records from the Global Biodiversity Information Facility ( GBIF), to import shape files with species range maps such as those available at the website of the International Union for Conservation of Nature ( IUCN), to import KML files, to import CSV files with records of the users, to import ESRI ASCII grid probability files generated by distribution modelling software and show the resulting records on a map., modestr supports five different methods for cleaning the data: (i) data filtering when downloading records from GBIF, (ii) habitat data filtering, (iii) taxonomic disambiguation filtering, (iv) automatic spatial dispersion and environmental layer filters and (v) custom data filtering. [ABSTRACT FROM AUTHOR]

1. Compilation of vegetation databases has contributed significantly to the advancement of vegetation science all over the world. Yet, methodological problems result from the use of plant names, particularly in data that originate from numerous and heterogeneous sources. One of the main problems is the inordinate number of synonyms that can be found in vegetation lists. 2. We present Taxonstand, an r package to automatically standardise plant names using The Plant List (http://www.theplantlist.org). The scripts included in this package allow connection to the online search engine of the Plant List and retrieve information from each species about its current taxonomic status. In those cases where the species name is a synonym, it is replaced by the current accepted name. In addition, this package can help correcting orthographic errors in specific epithets. 3. This tool greatly facilitates the preparation of large vegetation databases prior to their analyses, particularly when they cover broad geographical areas (supranational or even continental scale) or contain data from regions with rich floras where taxonomic problems have not been resolved for many of their taxa. Automated workflows such as the one provided by the taxonstand package can ease considerably this task using a widely accessible working nomenclatural authority list for plant species names such as The Plant List. [ABSTRACT FROM AUTHOR]

Summary The effect of several data collection and processing choices has been examined for high-resolution electron back-scatter pattern (EBSP) investigation of a highly deformed sample. The results were compared with a transmission electron microscope (TEM) investigation of the same sample. The estimated dislocation cell size was examined as a function of data cleaning strategy, line intercept vs. reconstruction method, critical misorientation angle definition and step-size. The best agreement with the TEM results was obtained using a modified relative reconstruction algorithm on fine step-size maps allowing some of the noise in the data to be overcome. Step sizes of up to one-quarter the average cell size yielded similar values for the estimated average cell size. As a result of the mixture of both high- and low-angle boundaries, single diffraction condition TEM images may give larger cell size estimates than the EBSP data. Orientation noise in the EBSP data, however, still limits the extent to which quantitative information can be extracted. [ABSTRACT FROM AUTHOR]

The editors explain the importance of identifying careless responding in addictions research. They refer to a participant's effortless or inattentive response behavior as a characteristic of careless responding. They describe the within-measure and post-hoc detection methods for screening data visually for possible errors and distinguishes categories of post-hoc techniques. Factors considered when selecting data cleaning strategies include response type, questionnaire length and missing data.