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About 10,000 papers for “Count data”
Colin J. Torney (204262), Andrew P. Dobson (67928), Felix Borner (3167691) et al. · Figshare · 2016
<p>Human and automated counts for each image for 500, 1000, 1500, 2000, 2500, and 3000 training samples.</p> <p>(ZIP)</p>
Cited by 0Open access
Alexandre, Nicolas · Figshare · 2025
Attached are code used for analyzing U.S. Census, Christmas bird count, Eucalyptus counts, and feeder counts. Attached code was used for predicting fitting models to data and predicting missing data for interim years. ANHU abundance index (Abundance_index.R) uses modified code from https://dx.doi.org/10.5061/dryad.kt06961.
Cited by 0Open access
Durand, Pierre M., Choudhury, Rajdeep, Rashidi, Armin et al. · DRYAD · 2014
Cited by 0Open access
Alexandre, Nicolas · Figshare · 2025
Attached are code used for analyzing U.S. Census, Christmas bird count, Eucalyptus counts, and feeder counts. Attached code was used for predicting fitting models to data and predicting missing data for interim years. ANHU abundance index (Abundance_index.R) uses modified code from https://dx.doi.org/10.5061/dryad.kt06961.
Cited by 0Open access
Duncan Lee, Ruth Salway, Gavin Shaddick et al. · 2008
A Bayesian moving average model for correlated count data In this paper we propose a new regression modeling approach for responses that are a correlated time series of counts. The approach is based on a hierarchical Bayesian model that incorporates a latent moving average process with gamma random errors. This contrasts with the majority of existing models that remove correlation with an autoregressive process based on log-normal errors, and is likely to be appropriate if the response data exhibit a moving average correlation structure. Furthermore, the specification of gamma random errors has the dual advantages of having a tractable theoretical correlation structure and not suffering from identifiability problems. In addition to presenting this model we discuss and compare three general strategies for regression modeling when the responses are a time series of counts, namely (i) assume the observations are independent and specify a sufficiently complex trend; (ii) assume the trend is constant and represent the remaining variation in the data via a correlation model; or (iii) model the trend and correlation separately. We then illustrate both our model and these three general strategies by presenting a new study of the short-term effects of PM10 on respiratory mortality in Greater London between 2001 and 2005, which provides an analysis of more recent data than in existing studies. 1
Cited by 0
Vishal Sarsani · Figshare · 2021
Count Data files from the Tnseq datsets
Cited by 0Open access
Joseph M Hilbe · 2010
Cited by 14Open access
Armstrong, Isaac · Figshare · 2023
Chironomid count data and head capsule abundance calculations for the three sediment cores discussed in this study.
Cited by 0Open access
Roland Proud (11764892), Fiona Allan (473265), Andrew Whiston (22177671) et al. · Figshare · 2025
Contains snail count data collected from 50 sites across 11 surveys between 2019 and 2023. Key columns include: Site.name (unique site ID), Period (before or after intervention), Site.type (intervention or control), Snail.count (number of Biomphalaria snails found), and Scoop.effort (relative sampling effort). (CSV)
Cited by 0Open access
John Kratz, Carly Strasser · Scientific Data · 2015
cience is built on a foundation of data. The production and publication of that data should be recognized as valuable scholarship, but data lacks an essential prerequisite for modern-day scholarly recognition-accepted metrics of significance. The scientific community has traditionally estimated the impact of a journal article by counting the number of subsequent references to it; more recently, a suite of web-based alternative metrics ('altmetrics') offer faster assessment and the chance capture other kinds of impact 1 . Data can be fit into these article-centered assessment systems by proxy, via data descriptor articles in journals like Earth Systems Science Data or Scientific Data 2,3 . Another approach is to apply familiar metrics directly to datasets published in online databases or repositories. Complicating matters, the same metric may mean different things with respect to articles versus datasets. A researcher can read an article online closely without downloading the PDF version, but if they view a dataset landing page without downloading the data, their level of engagement is almost certainly low. A better understanding of how to measure data impact is critical if we are to reward data creators and incentivize data publication.
Cited by 38Open access
Yi-hui Zhou, Kai Xia, Fred A. Wright · 2011
Cited by 0
Qiao Dong, Xueqin Chen, Baoshan Huang · Elsevier eBooks · 2024
Cited by 0
Joachim Mueller · Figshare · 2014
Raw photon count data for the publication “Quantitative measurement of brightness from living cells in the presence of photodepletion”. Data are stored as 16-bit unsigned binary integers without header. The data are grouped according to the figure in which they appear and represent a ‘minimal data set’. Each folder is named by the figure number. Details regarding the sample and data acquisition parameters can be found in the publication.
Cited by 0Open access
Shz · Figshare · 2023
This is the dark count data we use in our method.
Cited by 0Open access
Shz · Figshare · 2023
This is the dark count data we use in our method.
Cited by 0Open access
Mevin B. Hooten, Trevor J. Hefley · 2019
Cited by 0
2008
Cited by 1
Alex C. Michalos, Alex C. Michalos · 2014
Cited by 0
Kratz, John Ernest E III, Strasser, Carly · eScholarship (California Digital Library) · 2015
Science is built on a foundation of data. The production and publication of that data should be recognized as valuable scholarship, but data lacks an essential prerequisite for modern-day scholarly recognitionâaccepted metrics of significance. The scientific community has traditionally estimated the impact of a journal article by counting the number of subsequent references to it; more recently, a suite of web-based alternative metrics (âaltmetricsâ) offer faster assessment and the chance capture other kinds of impact1. Data can be fit into these article-centered assessment systems by proxy, via data descriptor articles in journals like Earth Systems Science Data or Scientific Data2,3. Another approach is to apply familiar metrics directly to datasets published in online databases or repositories. Complicating matters, the same metric may mean different things with respect to articles versus datasets. A researcher can read an article online closely without downloading the PDF version, but if they view a dataset landing page without downloading the data, their level of engagement is almost certainly low. A better understanding of how to measure data impact is critical if we are to reward data creators and incentivize data publication.
Cited by 0
Illinois. Division of Highways. Bureau of Planning. · The Journal of the Abraham Lincoln Association
Cited by 0Open access