1,720,958 research outputs found
Role of motor execution in the ocular tracking of self-generated movements
Full text link<p>Dataset relative to the following publication:</p>
<p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). Role of motor execution in the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>DOI: 10.1152/jn.00574.2016</p>
<p>Please refer to "data description.txt" for details about the data. Additional information can be deducted from the experimental scripts.</p>
Attention is allocated closely ahead of the target during smooth pursuit eye movements: evidence from EEG frequency tagging
Full text link<p>Dataset relative to the following publication:</p>
<p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2017). Attention is allocated closely ahead of the target during smooth pursuit eye movements: Evidence from EEG frequency tagging. <strong>Neuropsychologia</strong>, doi: 10.1016/j.neuropsychologia.2017.06.024.</p>
<p>Please refer to "data description.txt" in each experiment for details about the data. </p>
Enhanced brain responses to color during smooth pursuit eye movements
No full text<p>Dataset relative to the following publication:</p>
<p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2017). Enhanced brain responses to color during smooth pursuit eye movements. <em>Journal of Neurophysiology, </em>DOI: 10.1152/jn.00208.2017</p>
<p>Please refer to "data description.txt" for details about the data. </p>
LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements
No full text<p>Dataset relative to the following publication:</p>
<p>Chen, J., Valsecchi, M. & Gegenfurtner, K.R. (2016). LRP predicts smooth pursuit eye movement onset during the ocular tracking of self-generated movements. <em>Journal of Neurophysiology, </em>in press</p>
<p>Each folder contains the data relative to one experiment and the script that was used to generate them. Please refer to "Description on data format.txt" for the usage of the data.</p>
<p>Additional information can be deducted from the experimental scripts.</p>
Area dominates Edge in Pointillistic Colour
Full text link<p>Dataset relative to the following publication:</p>
<p>Koenderink, J, van Doorn, A. & Gegenfurtner, K.R. (2018) Area dominates edge in pointilistic colour. i-Perception, 9(4), 1–41. doi: 10.5281/zenodo.1293301</p>
<p>This is all that is needed to reproduce the figures and the numbers mentioned in the manuscript.<br>
These is also sufficient data to let one rerun the experiments with original instructions and programs and to analyse the results as described in the paper.</p>
<p>All that is needed is to install Processing and Mathematica and edit filenames at various places in the sources (this is really minimal work).</p>
<p>Here is a list of “what there is”:</p>
<p>- Analysis: Has initial analysis of the raw data. These are Mathematica (Wolfram, requires licence, free reader available from Wolfram site) notebooks.</p>
<p>- Data: Contains all raw data, as text files and as CSV summaries.</p>
<p>- Dots: Contains the programs used in the experiments. Written in Processing, they will run on any platform that has Processing (MIT, public domain) installed.</p>
<p>- Notebooks: Contain all analysis. These are Mathematica notebooks (Wolfram, licence required, free reader available from Wolfram site).</p>
<p>- Protocol: An rtf-document used as an instruction sheet for the participants.</p>
<p> </p>The work was supported by the DFG Collaborative Research Center SFB TRR 135 headed by Karl Gegenfurtner (Justus-Liebig Universita ̈t Giessen, Germany) and by the program by the Flemish Government (METH/14/02), awarded to Johan Wagemans. Jan Koenderink is supported by the Alexander von Humboldt Foundation
Seeing lightness in the dark
No full text<p>These are data files for a manuscript about lightness perception under scotopic viewing conditions. The main finding was that observers do not perceive white during scotopic adaptation, implicating that the cones must be activated to produce the appearance of white. The publication will be published in Current Biology as a Correspondence under the title, "Seeing lightness in the dark," on June 19th, 2017. The data file contents are as follows:</p>
<p>chipBlocks.csv - This CSV file contains the reflectances of our paper stimuli, relative to the reflectance of our PR650 white reference. Those numbers are in the first column. The remaining columns are for plotting purposes (third column = y-coordinate of squares along bottom of Fig. 1A in the associated paper, remaining columns = RGB values for coloring those squares).</p>
<p>chipsVSA4.tsv - A TSV file containing the relevant ratings for Fig. 1B in the associated paper. The first column is the x-axis position for plotting, the second column is population mean white rating, and the third column is standard error of the mean for that rating. The first set of two rows are for peripheral viewing of the chips, the second set of two rows are for foveal viewing of the chips, and the third set of two rows are for ratings given to the larger A4 papers. The ratings are grouped in sets of two because each x-position is repeated twice: once for the rating of the darkest stimulus and a second time for the lightest stimulus. The gray values given to the data points in Fig. 1B were computed by taking the average white rating for that point and dividing it by 100, giving a value in the range of 0-1, which can be used as a normalized grayscale RGB value.</p>
<p>obsDataChips.csv - This is a CSV file containing each individual white rating (from 0%-100% in steps of 10%) that observers gave to the chips, with two trials per chip across four different lightness levels and at two different positions of fixation. In the final report, fixation position was only considered for the scotopic condition. A header is in the file labelling the columns.</p>
<p>fovealPopDataChips.csv - A CSV file containing white ratings of the chip stimuli when viewed with the fovea. First column is the reflectance of our paper stimuli, as in chipBlocks.csv. The remaining columns are the population mean and standard error of the mean (SEM) for the different paper stimuli under the different adaptation conditions, as follows: 2nd/3rd column = population mean and SEM for scotopic adaptation, 4th/5th column = population mean and SEM for mesopic adaptation, 6th/7th column = population mean and SEM for dim photopic adaptation, 8th/9th column = population mean and SEM for bright photopic adaptation.</p>
<p>obsDataA4.csv - Same as obsDataChips.csv, but for the A4 sized papers. Observers were not given explicit instructions about different fixation positions during this experiment, so fixation was always listed as "center" and this column was ignored in later analysis.</p>
<p>popDataA4.tsv - Same as fovealPopDataChips.csv, but for the A4 sized papers.</p>
<p>whiteOLED.zip - A collection of MATLAB MAT files with data for each of the 17 subjects that performed the white patch adjustment task on an OLED, as described in the Supplementary Info of the associated paper. Each MAT file contains a 2x3 matrix variable named, "v", which holds the "luminance (v)alue" that each observer choose as their impression of white (see Supplementary Info for more details). The first row contains values for the larger patch and the second row contains the values for the smaller patch (each tested 3 times). The values are in normalized units of 0-1 for our monitor. To convert to luminance, use the following transformation: 2.04698e-5*((v*1023)^2.28231).</p>
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
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