Cytogenetics of the Simulium arcticum complex (Diptera: Simuliidae)

Zootaxa ◽  
2021 ◽  
Vol 5039 (3) ◽  
pp. 395-408
Author(s):  
GERALD F. SHIELDS
Keyword(s):  
Reproductive Isolation ◽  
Chromosomal Rearrangements ◽  
Geographic Range ◽  
Reproductive Status ◽  
Data Sets ◽  
Black Flies ◽  
Data Set ◽  
Northern Mexico ◽  
Chromosomal Data ◽  
The Many

Descriptions of chromosomal rearrangements, geographic distributions and frequencies of nine siblings and 28 cytotypes of the Simulium arcticum Malloch complex are presented. Findings are based on six data sets that include approximately 21,000 chromosomally analyzed larvae from throughout the known geographic range of S. arcticum. This is the largest chromosomal data set for any North American complex of black flies. This summary emphasizes the need to chromosomally analyze taxa of black flies since this type of analysis can result in, not only, a better understanding of the number of taxa in a complex and their relationships but also, it may help to understand the initial stages of reproductive isolation within otherwise morphologically identical groups. Geographically, the streams of eastern Alaska, the entire province of the Yukon and northern Mexico should be sampled. Taxonomically the many cytotypes should be tested for reproductive status when they occur in sympatry with other siblings and cytotypes of the complex. Finally, comparative multi-omic research would be useful.  

scholarly journals WHATEVER YOU WANT: INCONSISTENT RESULTS IS THE RULE, NOT THE EXCEPTION, IN THE STUDY OF PRIMATE BRAIN EVOLUTION

2018 ◽  
Author(s):  
Andreas Wartel ◽  
Patrik Lindenfors ◽  
Johan Lind
Keyword(s):  
Body Size ◽  
Brain Evolution ◽  
Data Sets ◽  
Data Set ◽  
Scientific Rigor ◽  
Explanatory Variables ◽  
Primate Brain ◽  
Residual Variation ◽  
The Many ◽  
Brain Data

AbstractPrimate brains differ in size and architecture. Hypotheses to explain this variation are numerous and many tests have been carried out. However, after body size has been accounted for there is little left to explain. The proposed explanatory variables for the residual variation are many and covary, both with each other and with body size. Further, the data sets used in analyses have been small, especially in light of the many proposed predictors. Here we report the complete list of models that results from exhaustively combining six commonly used predictors of brain and neocortex size. This provides an overview of how the output from standard statistical analyses changes when the inclusion of different predictors is altered. By using both the most commonly tested brain data set and a new, larger data set, we show that the choice of included variables fundamentally changes the conclusions as to what drives primate brain evolution. Our analyses thus reveal why studies have had troubles replicating earlier results and instead have come to such different conclusions. Although our results are somewhat disheartening, they highlight the importance of scientific rigor when trying to answer difficult questions. It is our position that there is currently no empirical justification to highlight any particular hypotheses, of those adaptive hypotheses we have examined here, as the main determinant of primate brain evolution.

scholarly journals Forecasting Principles from Experience with Forecasting Competitions

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 138-165
Author(s):  
Jennifer L. Castle ◽  
Jurgen A. Doornik ◽  
David F. Hendry
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Annual Data ◽  
Data Sets ◽  
Generation Process ◽  
Data Generation ◽  
Data Set ◽  
Economic Forecasting ◽  
The Many ◽  
Many Sources

Economic forecasting is difficult, largely because of the many sources of nonstationarity influencing observational time series. Forecasting competitions aim to improve the practice of economic forecasting by providing very large data sets on which the efficacy of forecasting methods can be evaluated. We consider the general principles that seem to be the foundation for successful forecasting, and show how these are relevant for methods that did well in the M4 competition. We establish some general properties of the M4 data set, which we use to improve the basic benchmark methods, as well as the Card method that we created for our submission to that competition. A data generation process is proposed that captures the salient features of the annual data in M4.

The Svd Analysis of Two Fields

2011 ◽  
Author(s):  
Gidon Eshel
Keyword(s):  
Data Sets ◽  
Eof Analysis ◽  
Data Set ◽  
Svd Analysis ◽  
Cross Covariance ◽  
State Evolution ◽  
The Many ◽  
Value Decomposition ◽  
Svd Method

Chapter 11 discussed one of the many methods available for simultaneously analyzing more than one data set. While powerful and useful (especially for unveiling favored state evolution pathways), the extended empirical orthogonal function (EEOF) procedure has some important limitations. Notably, because the state dimensions rapidly expand as state vectors are appended end-to-end, EEOF analysis may not always be numerically tractable. For analyzing two data sets, taking note of their cross-covariance but not explicitly of individual sets’ covariance, the singular value decomposition (SVD) method is the most natural. This chapter discusses SVD analysis of two fields. SVD analysis can be thought of as a generalization of EOF analysis to two data sets that are believed to be related.

scholarly journals Improving estimation of species distribution from citizen-science records using data-integration models

2021 ◽  
Author(s):  
Viviane Zulian ◽  
David A. W. Miller ◽  
Goncalo Ferraz
Keyword(s):  
Data Integration ◽  
Spatial Autocorrelation ◽  
Citizen Science ◽  
Geographic Range ◽  
Sampling Effort ◽  
Data Sets ◽  
Science Data ◽  
Integration Model ◽  
Data Set ◽  
Joint Likelihood

Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each data set, including observation technique and uncertainty about the observations. Our analysis illustrates 1) the incorporation of sampling effort, spatial autocorrelation, and site covariates in a joint-likelihood, hierarchical, data-integration model; 2) the evaluation of the contribution of each data set, as well as the contribution of effort covariates, spatial autocorrelation, and site covariates to the predictive ability of fitted models using a cross-validation approach; and 3) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future field work. Our results reveal a Vinaceous-breasted Parrot geographic range of 434,670 square kilometers, which is three times larger than the Extant area previously reported in the IUCN Red List. The exclusion of one data set at a time from the analyses always resulted in worse predictions by the models of truncated data than by the full model, which included all data sets. Likewise, exclusion of spatial autocorrelation, site covariates, or sampling effort resulted in worse predictions. The integration of different data sets into one joint-likelihood model produced a more reliable representation of the species range than any individual data set taken on its own improving the use of citizen science data in combination with planned survey results.

scholarly journals A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

2021 ◽  
pp. 412-418
Author(s):  
Aastha Gupta ◽  
Himanshu Sharma ◽  
Anas Akhtar
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Analytical Techniques ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
The Many ◽  
Data Elements

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

The Multiverse of Methods: Extending the Multiverse Analysis to Address Data-Collection Decisions

2020 ◽  
Vol 15 (5) ◽  
pp. 1158-1177
Author(s):  
Jenna A. Harder
Keyword(s):  
Data Collection ◽  
Data Cleaning ◽  
Real Data ◽  
Stage I ◽  
Data Sets ◽  
Data Set ◽  
Address Data ◽  
The Many ◽  
Shooting Decisions ◽  
Collection Methods

When analyzing data, researchers may have multiple reasonable options for the many decisions they must make about the data—for example, how to code a variable or which participants to exclude. Therefore, there exists a multiverse of possible data sets. A classic multiverse analysis involves performing a given analysis on every potential data set in this multiverse to examine how each data decision affects the results. However, a limitation of the multiverse analysis is that it addresses only data cleaning and analytic decisions, yet researcher decisions that affect results also happen at the data-collection stage. I propose an adaptation of the multiverse method in which the multiverse of data sets is composed of real data sets from studies varying in data-collection methods of interest. I walk through an example analysis applying the approach to 19 studies on shooting decisions to demonstrate the usefulness of this approach and conclude with a further discussion of the limitations and applications of this method.

scholarly journals OVATION: Oval variation, assessment, tracking, intensity, and online nowcasting

2002 ◽  
Vol 20 (7) ◽  
pp. 1039-1047 ◽  
Author(s):  
P. T. Newell ◽  
T. Sotirelis ◽  
J. M. Ruohoniemi ◽  
J. F. Carbary ◽  
K. Liou ◽  
...  
Keyword(s):  
Real Time ◽  
Calibration Procedure ◽  
Auroral Oval ◽  
Data Sets ◽  
Time Data ◽  
Data Set ◽  
Variation Assessment ◽  
Auroral Phenomena ◽  
The Many ◽  
Cross Calibration

Abstract. The location of the auroral oval and the intensity of the auroral precipitation within it are basic elements in any adequate characterization of the state of the magnetosphere. Yet despite the many ground-based and spacecraft-borne instruments monitoring various aspects of auroral behavior, there are no clear and consistent answers available to those wishing to locate the auroral oval or to quantify its intensity. The purpose of OVATION is to create a tool which does so. OVATION is useful both for archival purposes and for space weather nowcasting. The long-running DMSP particle data set, which covers both hemispheres, and has operated since the early 1980s, and which will continue to operate well into the next decade, is chosen as a calibration standard. Other data sets, including global images from Polar UVI, SuperDARN boundaries, and meridian scanning photometer images, are cross-calibrated to the DMSP standard. Each incorporated instrument has its average offset from the DMSP standard determined as a function of MLT, along with the standard deviations. The various data can, therefore, be combined in a meaningful manner, with the weight attached to a given boundary measurement varying inversely with the variance (square of the standard deviation). OVATION currently spans from December 1983 through the present, including real-time data. Participation of additional experimenters is highly welcomed. The only prerequisites are a willingness to conduct the prescribed cross-calibration procedure, and to make the data available online. The real-time auroral oval location can be found here: http://sd-www.jhuapl.edu/Aurora/ovation live/northdisplay.html.Key words. Magnetospheric physics (auroral phenomena; energetic particles, precipitating; magnetosphere – ionosphere interactions)

scholarly journals Data Set for the Reporting of Carcinomas of the Major Salivary Glands: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting

2018 ◽  
Vol 143 (5) ◽  
pp. 578-586 ◽  
Author(s):  
Raja R. Seethala ◽  
Albina Altemani ◽  
Robert L. Ferris ◽  
Isabel Fonseca ◽  
Douglas R. Gnepp ◽  
...  
Keyword(s):  
Salivary Gland ◽  
International Collaboration ◽  
Data Sets ◽  
Tumor Type ◽  
Anatomic Site ◽  
Data Set ◽  
Salivary Gland Carcinomas ◽  
The Many ◽  
Histologic Types ◽  
Cancer Reporting

The International Collaboration on Cancer Reporting is a nonprofit organization whose goal is to develop evidence-based, internationally agreed-upon standardized data sets for each anatomic site, to be used throughout the world. Providing global standardization of pathology tumor classification, staging, and other reporting elements will lead to achieving the objective of improved patient management and enhanced epidemiologic research. Salivary gland carcinomas are relatively uncommon, and as such, meaningful data about the many histologic types are not easily compared. Morphologic overlap between tumor types makes accurate classification challenging, but there are often significant differences in patient outcomes. Therefore, issues related to tumor type, tumor grading, high-grade transformation, extent of invasion, number and size of nerves affected, and types of ancillary studies are discussed in the context of daily application to specimens from these organs. This review focuses on the data set developed for salivary gland carcinomas with discussion of the key core and noncore elements developed for inclusion by an international expert panel of head and neck and oral-maxillofacial pathologists and surgeons.

scholarly journals Big Data Clustering And Its Applications Examination

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3687-3693
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Data Sets ◽  
Clustering Methods ◽  
Time Saving ◽  
Data Set ◽  
The Many

Clustering is a type of mining process where the data set is categorized into various sub classes. Clustering process is very much essential in classification, grouping, and exploratory pattern of analysis, image segmentation and decision making. And we can explain about the big data as very large data sets which are examined computationally to show techniques and associations and also which is associated to the human behavior and their interactions. Big data is very essential for several organisations but in few cases very complex to store and it is also time saving. Hence one of the ways of overcoming these issues is to develop the many clustering methods, moreover it suffers from the large complexity. Data mining is a type of technique where the useful information is extracted, but the data mining models cannot utilized for the big data because of inherent complexity. The main scope here is to introducing a overview of data clustering divisions for the big data And also explains here few of the related work for it. This survey concentrates on the research of several clustering algorithms which are working basically on the elements of big data. And also the short overview of clustering algorithms which are grouped under partitioning, hierarchical, grid based and model based are seenClustering is major data mining and it is used for analyzing the big data.the problems for applying clustering patterns to big data and also we phase new issues come up with big data

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

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