1,720,964 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna
No full textInitial release</p
Data and code for "Downsized: Gray whales using an alternative foraging ground have smaller morphology"
No full textOverview
This project compares length-at-age growth, as well as skull and fluke size, between ENP and PCFG gray whales.
Code
1. growth_model_v4.R – to run growth model
2. model_outputs_v4.Rmd – uses outputs from growth_model_v4.R to create summary data frames and figures in manuscript.
Data description
enp_Agbayani_etal_2020.csv = dataset compiled and used by Agbayani et al., 2020 to analyze ENP length-at-age growth from data comprised of standings, whaling, and aerial photogrammetry between 1926 and 1997. Contact Selina Agbayani for further details: [email protected]. Agbayani S, Fortune SME, Trites AW. 2020. Growth and development of North Pacific gray whales (Eschrichtius robustus). J Mammal 101, 742–754. (doi:10.1093/jmammal/gyaa028). https://doi.org/10.1093/jmammal/gyaa028
enp_length&age.csv = scientific whaling data on ENP gray whales collected by Rice and Wolman (1971). Contains total length (TL), sex, and age data for individuals.
enp_morphological_data.csv = scientific whaling data on ENP gray whales collected by Rice and Wolman (1971). Contains total length (TL), snout-blowhole (SnB), and fluke span (FS) measurements, as well as metadata such as sex, age, demographic info, and date of specimen collection.
pcfg_data_growth_model.csv = morphometric and age data for PCFG gray whales collected via drone-based photogrammetry and photo-ID history. Contains total length (TL), snout-blowhole (SnB), and fluke span (FS) measurements, as well as metadata such as sex and age,
Most columns are self explanatory. Below are descriptions of columns that are less obvious that may be in each or some of the .csv files.
whale_ID: ID of whale
AID: whale_ID + date (YYYYMMDD)
population: ENP or PCFG
Age: age of whale in years
AgeType: 0 = “known age”, 1 = ‘min age’ for PCFG. For ENP, earplugs are considered known age.
TL.mean: mean of posterior distribution for TL for that individual
TL.lower: lower bound of 95% highest posterior density interval (HPDI)
TL.upper: upper bound of 95% HPDI
TL.cv: coefficient of variation (%) of posterior distribution for TL for that individual
TL.var: variance of posterior distribution for TL for that individual
TL.sd: standard deviation of posterior distribution for TL for that individual
SnB.std: snout-blowhole measurement standardized by TL. Same suffixes apply (i.e, SnB.std.mean = the mean of the posterior distribution for SnB for that individual)
Fs.std: fluke span measurement standardized by TL. Same suffixes apply (i.e, Fs.std.mean = the mean of the posterior distribution for Fs for that individual)
suffix “.HPDwidth”: the width of the HPDI (.upper - .lower)
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Western_Antarctic_Peninsula_humpback_whale_body_condition_data.csv
No full textDataset used in analysis from:
Bierlich KC, Hewitt J, Schick R, Pallin L, Dale J, Friedlaender A, Christiansen F, Sprogis KR, Dawn AH, Bird CN, Larsen GD, Nichols R, Shero MR, Goldbogen J, Read AJ and Johnston DW (2022) Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Front. Mar. Sci. 9:1036860. doi: 10.3389/fmars.2022.1036860
Since the output of the uncertainty model is a posterior distribution for each measurement, we summarize each measurement using the mean of the posterior distribution and describe the uncertainty as the variance and 95% highest posterior density (HPD) intervals. So, for total length (TL), use TL.mean as the equivalent of a point estimate for TL. Use TL.var for the variance, and TL.lower and TL.upper for the lower and upper HPD interval, respectively. TL.cv is the coefficient of variation (CV%) of the posterior distribution. Same goes for body condition, which we measure as body area index (BAI).
Explanation of some other headers:
class = reproductive clas
pregnant = pregnancy status from biopsy sample analysis of progesterone
mom_calf = indicates if individual is a "mom" or a "calf"
mom_calf_pair = indicates the mom and calf pair number
datetime = date w/ GMT time
season = austral spring to fall; Season 1 (2016-2017), Season 2 (2017-2018), Season 3 (2018-2019)
seasonal_duration = the number of days since the "start" of the season, which we set as Nov. 1
Lab = Duke University Marine Robotics and Remote Sensing (MaRRS) Lab or Fredrik Christiansen (FC)
Aircraft = type of drone
Flight = the flight ID
Focal_Length = focal length (mm) of the camera
Sw = sensor width of camera (mm)
Iw = image width of camera (px)
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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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