1,720,969 research outputs found
UDASH-SalaciaML-2-Arctic: Unified Database for Arctic and Subarctic Hydrography - Simplified and extended for quality control analyses
No full textUDASH-SalaciaML-2-Arctic represents an updated and simplified version of the UDASH dataset, that has been created to develop an artificial intelligence algorithm, that we name SalaciaML-2-Arctic to support the visual/human quality control (QC) of the data. UDASH-SalaciaML-2-Arctic can be directly used with our algorithms, provided under the GitHub repository (https://github.com/GastonKreps/SalaciaML-2-Arctic), to reproduce our results, extend the methods and more. Additionally, we have implemented SalaciaML-2-Arctic as an user-friendly app at https://mvre.autoqc.cloud.awi.de. The following steps have been applied to the original UDASH dataset to create UDASH-SalaciaML-2-Arctic:
- Single, annual .txt files were concatenated into two single .csv files, one for temperature and one for salinity.
- Parameters have been reduced to a minimal set, names and units have been adapted slightly.
- Temperature and salinity gradients have been calculated and added.
- Missing values are kept as -999.
- Traditional QC flags, generated by the classical algorithms, have been added: 0=good, 2=suspect_gradient, 4=spike
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Identifikation und statistische Auswertung von globalen Wasserdampftrends aus Satellitenmessungen
No full textGlobal water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather Service) is presented. The validation has been performed in a statistical way on the basis of univariate time series. Information about the probability of agreement between the two independently observed trends, conditional on the respective data, is revealed. On the one hand a standard t-test is used to compare the trends and on the other hand a Bayesian model selection approach has been developed to derive the probability of agreement. The hypothesis of equal trends from satellite and radiosonde water vapour data is preferred in 85 % of compared pairs of trends. Substantial evidence for the hypothesis of agreeing trends is found in 26 % of analysed trends. However, also disagreement has been observed, where the main reason has been identified on the one hand as the different spatial resolutions of the instruments. This means, that the radiosonde measurements can resolve very localised events, which is not possible with the satellite instruments. On the other hand, in contrast to the in principle continuously available (on a monthly mean basis) GOME/SCIAMACHY data, missing data in the radiosonde time series lead to trend discrepancies. The identification and validation of water vapour trends is an important step for a better understanding of climate change, but water vapour is not the only contributing quantity. Beside water vapour, decisive parameters are temperature, clouds, precipitation, vegetation and many more. A promising framework for the investigation of a multivariate data set of environmental variables is given by the Markov chain analysis. As a first approach, the Markov chain analysis has been applied to a bivariate water vapour -- temperature data set, where the global near surface temperatures are provided by the Goddard Institute of Space Studies (GISS) and cover a time span from 1880 to 2005. The temperature data are retrieved from ground stations and are mainly based on the Global Historical Climatology Network (GHCN). In the framework of a Markov chain analysis, the bivariate set of data is reduced to a univariate sequence of symbols, which can be described as a discrete stochastic process, a Markov chain. This Markov chain represents the water vapour -- temperature interaction or state of a region. Several descriptors have been calculated, such as persistence, replacement of and entropy. This approach is new in environmental science. Exemplarily two climate systems, the Iberian Peninsular and a region at the islands of Hawaii in the central Pacific Ocean, are investigated. The Markov chain analysis is able to retrieve significant differences between the two climate systems in terms of the characteristic descriptors, which reflect properties such as climate stability, rate of changes and short term predictability
Identification and statistical analysis of global water vapour trends based on satellite data
No full textGlobal water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather Service) is presented. The validation has been performed in a statistical way on the basis of univariate time series. Information about the probability of agreement between the two independently observed trends, conditional on the respective data, is revealed. On the one hand a standard t-test is used to compare the trends and on the other hand a Bayesian model selection approach has been developed to derive the probability of agreement. The hypothesis of equal trends from satellite and radiosonde water vapour data is preferred in 85 % of compared pairs of trends. Substantial evidence for the hypothesis of agreeing trends is found in 26 % of analysed trends. However, also disagreement has been observed, where the main reason has been identified on the one hand as the different spatial resolutions of the instruments. This means, that the radiosonde measurements can resolve very localised events, which is not possible with the satellite instruments. On the other hand, in contrast to the in principle continuously available (on a monthly mean basis) GOME/SCIAMACHY data, missing data in the radiosonde time series lead to trend discrepancies. The identification and validation of water vapour trends is an important step for a better understanding of climate change, but water vapour is not the only contributing quantity. Beside water vapour, decisive parameters are temperature, clouds, precipitation, vegetation and many more. A promising framework for the investigation of a multivariate data set of environmental variables is given by the Markov chain analysis. As a first approach, the Markov chain analysis has been applied to a bivariate water vapour -- temperature data set, where the global near surface temperatures are provided by the Goddard Institute of Space Studies (GISS) and cover a time span from 1880 to 2005. The temperature data are retrieved from ground stations and are mainly based on the Global Historical Climatology Network (GHCN). In the framework of a Markov chain analysis, the bivariate set of data is reduced to a univariate sequence of symbols, which can be described as a discrete stochastic process, a Markov chain. This Markov chain represents the water vapour -- temperature interaction or state of a region. Several descriptors have been calculated, such as persistence, replacement of and entropy. This approach is new in environmental science. Exemplarily two climate systems, the Iberian Peninsular and a region at the islands of Hawaii in the central Pacific Ocean, are investigated. The Markov chain analysis is able to retrieve significant differences between the two climate systems in terms of the characteristic descriptors, which reflect properties such as climate stability, rate of changes and short term predictability
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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