Use Of Datasets Research Articles

Ecological Niche Modeling of Ommatissus Lybicus (Hemiptera: Tropiduchidae) De Bergevin

Date palm hooper is one of the most important pests on date palms with many highly diverged populations varied in damaging rate and preferring host cultivar. Current and potential geographic distribution of date palm hopper, Ommatissus lybicus (de Bergevin; Hemiptera: Tropiduchidae) in Iran was modeled using the Maxent procedure. Point sampling data sets included latitude, longitude, and altitude and were augmented with data on 19 bioclimatic variables retrieved from WorldClim dataset for use as predictor variables in Maxent. Maxent results demonstrated that northwest Sistan-Baluchestan, east, south and southeast Kerman, northeast and northwest Hormozgan and small districts of Bushehr provinces are hot spot habitats for O. lybicus in Iran. Jackknife analysis revealed that precipitation of warmest quarter was the most influential explanatory variable in determining distribution of O. lybiucs in Iran. Moreover, curve response and frequency distribution analyses revealed that annual mean temperature was the most predictive factor of the distribution of O. lybicus. Discontinuity in predicted hot spot habitats of O. lybicus was consistent with the genetic structure recorded for this species. Our study represents valuable information that can inform pest management strategies aimed at preventing population expansion and niche shift.

Assembling Typical Meteorological Year Data Sets for Building Energy Performance Using Reanalysis and Satellite-Based Data

We present a method to generate Typical Meteorological Year (TMY) data sets for use in calculations of the energy performance of buildings, based on satellite derived solar radiation data and other meteorological parameters obtained from reanalysis products. The great advantage of this method is the availability of data over large geographical regions, giving global coverage for the reanalysis and continental-scale coverage for the solar radiation data, making it possible to generate TMY data for nearly any location, independent of the availability of meteorological measurement stations in the area. The TMY data generated with this method have been validated against 487 meteorological stations in Europe, by calculating heating and cooling degree days, and by running building energy performance simulations using EnergyPlus. Results show that the generated data sets using a long time series perform better than the TMY data generated from station measurements for building heating calculations and nearly as well for cooling calculations, with relative standard deviations remaining below 6% for heating calculations. TMY data constructed using the proposed method yield somewhat larger deviations compared to TMY data constructed from station data. We outline a number of possibilities for further improvement using data sets that will become available in the near future.

Assessing the Suitability of the WorldClim Dataset for Ecological Studies in Southern Kenya

There have been numerous efforts to generate freely available climatic datasets for use in species distribution models, the most popular being the global climatic dataset known as WorldClim. The availability of such datasets is invaluable to scientists as many studies are performed in remote areas where no weather stations are found. However, many users do not critically assess the suitability of these datasets for their applications, and errors associated with global datasets are often assumed to be negligible. Understanding what a climate dataset can or cannot deliver requires the user to have a working knowledge of what the basic spatial climate-forcing factors are at the scale of his/her study, and to have a good understanding of the uncertainty in the dataset. In geographic studies, uncertainty is often described by the degree of error (uncertainty), or degree of accuracy (certainty) in data, and thematic uncertainty refers to the uncertainty in measures made for each variable, whereas temporal uncertainty refers to the uncertainty in time period represented by each variable. Here, we used climatic data from weather stations to investigate the climate-forcing factors in southern Kenya, and then used this weather station data to investigate the uncertainty in the WorldClim dataset. Results indicated that the nineteen core Worldclim variables, known as bioclimatic variables, accurately depicted the local variations in climate in the study area. However, whereas the monthly and seasonal temperature variables represented the same time period in different locations, the same was not true for the monthly and seasonal precipitation variables. The onset of rains is a key biological indicator, and scientists studying phenomena tied to the onset of rains need to keep in mind the temporal variations represented in the WorldClim dataset.

Bovine Genome Database: Tools for Mining the Bos taurus Genome.

The Bovine Genome Database (BGD; http://bovinegenome.org ) is a web-accessible resource that supports bovine genomics research by providing genome annotation and data mining tools. BovineMine is a tool within BGD that integrates BGD data, including the genome, genes, precomputed gene expression levels and variant consequences, with external data sources that include quantitative trait loci (QTL), orthologues, Gene Ontology, gene interactions, and pathways. BovineMine enables researchers without programming skills to create custom integrated datasets for use in downstream analyses. This chapter describes how to enhance a bovine genomics project using the Bovine Genome Database, with data mining examples demonstrating BovineMine.

Small-scale trials on passenger microbehaviours during aircraft boarding and deplaning procedures

NRC researchers have recently employed computational tools to simulate airline passenger movement – primarily to examine the impact of different procedures on aircraft boarding times. It was felt that the modelling techniques could be improved by the inclusion of current, relevant and refined data on passenger movement. This article outlines a series of small-scale laboratory tests performed to help quantify individual passenger boarding and deplaning movement with and without the presence of luggage. Test subjects were monitored and video recorded while participants moved through a cabin section enabling observers to quantify the performance of specific micro-behaviours that formed the deplaning and boarding process. These tests were conducted at the National Research Council of Canada's prototype cabin research facility. The trials involved a total of 35 subjects who were each observed performing a series of 12 trials inside the cabin section. These trials were formed from the manipulation of three factors: seat pitch (29 in/31 in/33 in), luggage (present/absent), and instructions (continuous flow/discrete movements). During each trial, participants performed a series of micro-behaviours that included traversing the aisle/row, stowing/collecting under-seat bags, seat belt fastening and unfastening and adopting a brace position. In addition, two trials were conducted for each participant outside of the cabin facility to establish baseline straight line movement speeds with and without luggage. The study indicated that the impact of luggage and seat pitch had a notable, but complex, effect on performance and, more importantly, generated an array of data-sets for use in future simulation efforts. For instance, seat pitch appeared to have a more consistent impact when passenger movement was perpendicular to the seat row, as opposed to along the seat row where the impact was varied. Such data collection is necessary to advance empirical support for current and future simulation efforts.

Profiling of volatile fragrant components in a mini-core collection of mango germplasms from seven countries.

Aroma is important in assessing the quality of fresh fruit and their processed products, and could provide good indicators for the development of local cultivars in the mango industry. In this study, the volatile diversity of 25 mango cultivars from China, America, Thailand, India, Cuba, Indonesia, and the Philippines was investigated. The volatile compositions, their relative contents, and the intervarietal differences were detected with headspace solid phase microextraction tandem gas chromatography-mass spectrometer methods. The similarities were also evaluated with a cluster analysis and correlation analysis of the volatiles. The differences in mango volatiles in different districts are also discussed. Our results show significant differences in the volatile compositions and their relative contents among the individual cultivars and regions. In total, 127 volatiles were found in all the cultivars, belonging to various chemical classes. The highest and lowest qualitative abundances of volatiles were detected in ‘Zihua’ and ‘Mallika’ cultivars, respectively. Based on the cumulative occurrence of members of the classes of volatiles, the cultivars were grouped into monoterpenes (16 cultivars), proportion and balanced (eight cultivars), and nonterpene groups (one cultivars). Terpene hydrocarbons were the major volatiles in these cultivars, with terpinolene, 3-carene, caryophyllene and α-Pinene the dominant components depending on the cultivars. Monoterpenes, some of the primary volatile components, were the most abundant aroma compounds, whereas aldehydes were the least abundant in the mango pulp. β-Myrcene, a major terpene, accounted for 58.93% of the total flavor volatile compounds in ‘Xiaofei’ (Philippens). γ-Octanoic lactone was the only ester in the total flavor volatile compounds, with its highest concentration in ‘Guiya’ (China). Hexamethyl cyclotrisiloxane was the most abundant volatile compound in ‘Magovar’ (India), accounting for 46.66% of the total flavor volatiles. A typical aldehydic aroma 2,6-di-tert-butyl-4-sec-butylphenol, was detected in ‘Gleck’. A highly significant positive correlation was detected between Alc and K, Alk and Nt, O and L. Cultivars originating from America, Thailand, Cuba, India, Indonesia and the Philippines were more similar to each other than to those from China. This study provides a high-value dataset for use in development of health care products, diversified mango breeding, and local extension of mango cultivars.

Riqueza de especies arbóreas en bosques de montaña de Chiapas: estimaciones a partir de datos de herbarios e inventarios florísticos

Estimates of tree species richness (S) have usually been approached with systematic surveys. More recently, with the increasing on-line availability of digital herbaria data, alternative S estimates are possible. How comparable are these two estimates? We fitted generalized linear models (GLM) to obtain estimates of S with both data types from three different mountain regions in Chiapas (>1,500 elevation) for which substantial numbers of both herbarium vouchers and survey plots are available. A total number of 792 tree species were detected with both types of data. Clench's model estimated 862 and 425 tree species with herbarium and survey data, respectively. The selected GLM models rendered S estimates that were positively correlated in two out of the three studied regions (not in the most species-rich region). Systematic surveys provide a snap-shot of species equitability among real samples of forest communities, and therefore, represent a useful set of data to approach the understanding of tree diversity structure. On the other hand, herbarium vouchers allow estimates of how many rare or restricted species may still be absent in the survey-based inventory. It seems convenient to make complementary use of both types of data in order to estimate S in poorly known tropical forests.

Availability of datasets for digital forensics – And what is missing

This paper targets two main goals. First, we want to provide an overview of available datasets that can be used by researchers and where to find them. Second, we want to stress the importance of sharing datasets to allow researchers to replicate results and improve the state of the art. To answer the first goal, we analyzed 715 peer-reviewed research articles from 2010 to 2015 with focus and relevance to digital forensics to see what datasets are available and focused on three major aspects: (1) the origin of the dataset (e.g., real world vs. synthetic), (2) if datasets were released by researchers and (3) the types of datasets that exist. Additionally, we broadened our results to include the outcome of online search results. We also discuss what we think is missing. Overall, our results show that the majority of datasets are experiment generated (56.4%) followed by real world data (36.7%). On the other hand, 54.4% of the articles use existing datasets while the rest created their own. In the latter case, only 3.8% actually released their datasets. Finally, we conclude that there are many datasets for use out there but finding them can be challenging.

Open Data in Israel, Georgia and Uzbekistan: nature and scope

PurposeThe main purpose of this study is to investigate the nature and scope of Open Data in Israel, Georgia and Uzbekistan.Design/methodology/approachDrawing upon the two Open Data models proposed by Kalampokis et al. (2011) and Sieber and Johnson (2015), this paper seeks to underline the nature and scope of Open Data in Israel, Georgia and Uzbekistan.FindingsOpen Data across the three countries (Israel, Georgia and Uzbekistan) is at an early stage of maturity. There is more of data aggregation and this poses a number of challenges as far as access to and usage of these data sets are concerned. Furthermore, in terms of the model proposed by Sieber and Johnson (2015), there is “data over the wall” in these countries.Practical implicationsThe study holds practical relevance in the sense that policymakers are encouraged to further the Open Data initiatives in their respective countries. Furthermore, government departments are encouraged to release data sets for use by the public and to generate public value.Originality/valueWhereas extant literature on Open Data veers around economies in the West, academic research in other countries is visibly lacking. Also, no study has been undertaken so far wherein Open Data initiatives in Israel, Georgia and Uzbekistan have been undertaken till date. The present study seeks to plug this gap in a major way.

The complete mitochondrial genome of Odorrana hainanensis (Anura: Ranidae)

The Chinese Hainan odorous frog, Odorrana hainanensis, is an IUCN red-listed vulnerable species found primarily in forests in Hainan, China. In this study, we firstly report the complete mitochondrial genome of O. hainanensis, which was 17986 bp in length, containing 13 protein-coding genes, 22 transfer RNAs (tRNAs), two rRNA genes, and a putative control region. Phylogenetic analysis based on the concatenated data set of two rRNAs and 13 protein-coding genes indicated that the O. hainanensis was clustered with O. schmackeri. This work will provide a set of useful data on further molecular evolution studies of this endangered species.

A Hybrid Approach for Persian Named Entity Recognition

Named entity recognition (NER) is an information extraction subtask that attempts to recognize and categorize named entities in unstructured text into predefined categories such as the names of people, organizations, and locations. Recently, machine learning approaches, such as hidden Markov model (HMM) as well as hybrid methods, are frequently used to solve Name Entity Recognition. To the best of our knowledge, publicly available data sets for NER in Persian do not exist in any machine learning-based Persian NER system. Because of HMM innate weaknesses, in this paper, we have used both hidden Markov model and rule-based method to recognize named entities in Persian texts. The combination of rule-based method and machine learning method results in a high accurate recognition. The proposed system in its machine learning section uses HMM and Viterbi algorithms, and in its rule-based section employs a set of lexical resources and pattern bases for the recognition of named entities including the names of people, locations and organizations. During this study, we annotate our own training and testing data sets for use in the related phases. Our hybrid approach performs on Persian language with 89.73% precision, 82.44% recall, and 85.93% F-measure using an annotated test corpus including 32,606 tokens.

Dataset for reporting of thymic epithelial tumours: recommendations from the International Collaboration on Cancer Reporting (ICCR)

The International Collaboration on Cancer Reporting (ICCR) is a not-for-profit organization formed by the Royal Colleges of Pathologists of Australasia and the United Kingdom, the College of American Pathologists, the Canadian Association of Pathologists-Association Canadienne des Pathologists in association with the Canadian Partnership Against Cancer, and the European Society of Pathology. Its goal is to produce standardized, internationally agreed, evidence-based datasets for use throughout the world. This article describes the development of a cancer dataset by the multidisciplinary ICCR expert panel for the reporting of thymic epithelial tumours. The dataset includes 'required' (mandatory) and 'recommended' (non-mandatory) elements, which are validated by a review of current evidence and supported by explanatory text. Seven required elements and 12 recommended elements were agreed by the international dataset authoring committee to represent the essential information for the reporting of thymic epithelial tumours. The use of an internationally agreed, structured pathology dataset for reporting thymic tumours provides all of the necessary information for optimal patient management, facilitates consistent and accurate data collection, and provides valuable data for research and international benchmarking. The dataset also provides a valuable resource for those countries and institutions that are not in a position to develop their own datasets.

Characterization of the complete mitochondrial genome of Phodopus roborovskii (Rodentia: Cricetidae) and systematic implications for Cricetinae phylogenetics

Phodopus roborovskii (subfamily Cricetinae) is widely distributed in the northern arid regions of China. This study reports its complete mitochondrial genome (mitogenome) for the first time. The complete sequence was 16,273 bp long, including 13 protein-coding genes, 2 ribosomal RNAs, 22 transfer RNAs, and 1 major noncoding region. The base composition and codon usage were described. The putative origin of replication for the light strand (OL) of P. roborovskii was approximately 45 bp long and was highly conserved in the stem-loop and adjacent sequences, but the starting sequence of replication varied between genera among Rodentia. We analyzed the three domains of the D-loop region, and the results indicated that the central domain had higher G + C content and lower A + T content than two peripheral domains. Phylogenetic analyses indicated high resolution in four main divergent clades using mitogenomes data within Cricetidae. Within Cricetinae clade, P. roborovskii was at basal position which was in line with previous researches, and it shared a common ancestor with other extant hamsters. This work validated previous molecular and karyotype researches using mitogenomes data, and provided a set of useful data on phylogeny and molecular evolution in Cricetidae species.

Characterization and phylogenetic analysis of the complete mitogenome of Allactaga sibirica (Rodentia: Dipodidae)

Allactaga sibirica (Dipodidae) is widely distributed in the northwestern arid regions of China. The complete mitochondrial genome (mitogenome) of A. sibirica was 16,685 bp in length; included 13 protein-coding genes, 2 ribosome genes, 22 tRNA genes, and one control region; and had a structure that was typical of vertebrates. The base composition and codon usage of the mitogenome are described, and the structure of the non-coding sequence in the A. sibirica is reported for the first time. The putative origin of replication for the light strand of A. sibirica was approximately 45 bp long, and was highly conserved in the stem-loop and adjacent sequences. Phylogenetic analyses showed high resolution in each of the main divergent clades within Dipodoidea using mitogenomes data. The results indicated that the Zapodidae group was a representative of very basal taxon in Dipodoidea, and shared a common ancestor with Dipodidae species. Within Dipodidae clade, Allactaginae species was at basal position, and this result was in line with previous molecular systematic and morphological studies. Furthermore, Euchoreutes naso and A. sibirica had a close relationship could implicate a sister-group relationship between Euchoreutinae and Allactaginae. Meanwhile, this work also provided a set of useful data on phylogeny and molecular evolution in Dipodidae species.

Sequencing and characterization of mitochondrial DNA genome for Brama japonica (Perciformes: Bramidae) with phylogenetic consideration

In this study, the complete mitochondrial genome sequence of Brama japonica is isolated and characterized by PCR and primer-walking sequencing techniques. The complete DNA is 17,009 bp in length and contained 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes and a long putative control region. The gene organization and nucleotide composition are identical to those of other Bramidae fishes. In contrast, the 12S rRNA gene contains a big poly C structure which is larger than those from other Bramidae species. Of 37 genes, twenty-eight are encoded by heavy strand, while nine are encoded by light strand. Among the 13 protein-coding genes, twelve employ ATG as start codon, while only one (COI) utilizes GTG as start codon. The terminal associated sequence (TAS), the central and conserved sequence block (CSB-E and CSB-D) and a variable domain (CSB-1, CSB-2 and CSB-3) are identified in the control region, while the typical central conserved CSB-F is not detected. From the phylogenetic tree, we find that B. japonica, together with other five Bramidae species form a monophyletic group among 24 species. This work provides a set of useful data for studies on population genetic diversity and molecular evolution in Bramidae fish species.

The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory

Ecosystem monitoring networks aim to collect data on physical, chemical and biological systems and their interactions that shape the biosphere. Here we introduce the Australian SuperSite Network that, along with complementary facilities of Australia's Terrestrial Ecosystem Research Network (TERN), delivers field infrastructure and diverse, ecosystem-related datasets for use by researchers, educators and policy makers. The SuperSite Network uses infrastructure replicated across research sites in different biomes, to allow comparisons across ecosystems and improve scalability of findings to regional, continental and global scales. This conforms with the approaches of other ecosystem monitoring networks such as Critical Zone Observatories, the U.S. National Ecological Observatory Network; Analysis and Experimentation on Ecosystems, Europe; Chinese Ecosystem Research Network; International Long Term Ecological Research network and the United States Long Term Ecological Research Network. The Australian SuperSite Network currently involves 10 SuperSites across a diverse range of biomes, including tropical rainforest, grassland and savanna; wet and dry sclerophyll forest and woodland; and semi-arid grassland, woodland and savanna. The focus of the SuperSite Network is on using vegetation, faunal and biophysical monitoring to develop a process-based understanding of ecosystem function and change in Australian biomes; and to link this with data streams provided by the series of flux towers across the network. The Australian SuperSite Network is also intended to support a range of auxiliary researchers who contribute to the growing body of knowledge within and across the SuperSite Network, public outreach and education to promote environmental awareness and the role of ecosystem monitoring in the management of Australian environments.

Questions as data: illuminating the potential of learning analytics through questioning an emergent field

In providing a meta-analysis of a series of workshop papers and questions arising on the emergent field of learning analytics, this paper contributes to the ongoing formation of a shared research agenda. The first ICCE Learning Analytics workshop in 2014 demonstrated the effectiveness of a focused questioning session for collecting relevant data beyond the content of the papers themselves. In December 2014, approximately 40 participants attended the workshop held in Nara, Japan, and contributed to the collection of open research questions. Six papers were presented covering topics including scope; interoperability standards; privacy and control of individual data, extracting data from learning content and processes; and the development of conceptual frameworks. These papers established a base from which the group generated a set of questions that invite further investigation. Utilising the first stage of the Question Formulation Technique, a pedagogical approach designed to stimulate student inquiry, a prominent finding from the workshop that questions emerging from focused inquiry provide a useful set of data in their own right. With an explicit workshop focus on learning analytics interoperability, this paper reports on the emergent issues identified in the workshop and the kinds of questions associated with each issue in the context of current research in the field of learning analytics. The study considers the complexity arising from the fact that data associated with learning is itself becoming a digital learning resource while also enabling analysis of learner behaviours and systems usage.

On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports

Abstract This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3) are available at multiple levels (e.g., made during aircraft ascent and descent). Improvements in weather forecasts due to these data have already had major impacts on a variety of aspects of airline operations, ranging from fuel savings from improved wind and temperature forecasts used in flight planning to passenger comfort and safety due to better awareness of en route and near-terminal weather hazards. Aircraft wind and temperature observations now constitute the third most important dataset for global NWP and, in areas of ample reports, have become the single most important dataset for use in shorter-term, regional NWP applications. Automated aircraft reports provide the most cost-effective data source for improving NWP, being more than 5 times more cost effective than any other major-impact observing system. They also present an economical alternative for obtaining tropospheric profiles both in areas of diminishing conventional observation and as a supplement to existing datasets, both in time and space. An evaluation of moisture observations becoming available from an increasing number of AMDAR-equipped aircraft will be presented in Part II of this paper, including examples of the use of the full array of AMDAR observations in a variety of forecasting situations.

Optimal training dataset composition for SVM-based, age-independent, automated epileptic seizure detection.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-016-1468-y) contains supplementary material, which is available to authorized users.

Dataset for Reporting of Malignant Mesothelioma of the Pleura or Peritoneum: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

-The International Collaboration on Cancer Reporting is a not-for-profit organization formed by the Royal Colleges of Pathologists of Australasia and the United Kingdom; the College of American Pathologists; the Canadian Association of Pathologists-Association Canadienne des Pathologists, in association with the Canadian Partnership Against Cancer; and the European Society of Pathology. Its goal is to produce common, internationally agreed upon, evidence-based datasets for use throughout the world. -To describe a dataset developed by the Expert Panel of the International Collaboration on Cancer Reporting for reporting malignant mesothelioma of both the pleura and peritoneum. The dataset is composed of "required" (mandatory) and "recommended" (nonmandatory) elements. -Based on a review of the most recent evidence and supported by explanatory commentary. -Eight required elements and 7 recommended elements were agreed upon by the Expert Panel to represent the essential information for reporting malignant mesothelioma of the pleura and peritoneum. -In time, the widespread use of an internationally agreed upon, structured, pathology dataset for mesothelioma will lead not only to improved patient management but also provide valuable data for research and international benchmarks.