cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1590040.v1
title: New distinct compartments in the G<sub>2</sub> phase of the cell cycle defined by the levels of γH2AX.

description: Induction of DNA double strand breaks leads to phosphorylation and focus-formation of H2AX. However, foci of phosphorylated H2AX (γH2AX) appear during DNA replication also in the absence of exogenously applied injury. We measured the amount and the number of foci of γH2AX in different phases of the cell cycle by flow cytometry, sorting and microscopy in 4 malignant B-lymphocyte cell lines. There were no detectable γH2AX and no γH2AX-foci in G<sub>1</sub> cells in exponentially growing cells and cells treated with PARP inhibitor (PARPi) for 24 h to create damage and reduce DNA repair. The amount of γH2AX increased immediately upon S phase entry, and about 10 and 30 γH2AX foci were found in mid-S phase control and PARPi-treated cells, respectively. The γH2AX-labeled damage caused by DNA replication was not fully repaired before entry into G<sub>2</sub>. Intriguingly, G<sub>2</sub> cells populated a continuous distribution of γH2AX levels, from cells with a high content of γH2AX and the same number of foci as S phase cells (termed “G<sub>2</sub>H” compartment), to cells that there were almost negative and had about 2 foci (termed “G<sub>2</sub>L” compartment). EdU-labeling of S phase cells revealed that G<sub>2</sub>H was directly populated from S phase, while G<sub>2</sub>L was populated from G<sub>2</sub>H, but in control cells also directly from S phase. The length of G<sub>2</sub>H in particular increased after PARPi treatment, compatible with longer DNA-repair times. Our results show that cells repair replication-induced damage in G<sub>2</sub>H, and enter mitosis after a 2–3 h delay in G<sub>2</sub>L.
DOI: 10.6084/m9.figshare.1590037
title: The Diversity and Abundance of Insects in Danby Woods Based on the Moisture Levels of the Soil

description: On October 19 and October 26, 2015, ths students from Bailey Hewitt's laboratory 01, in Christopher Lortie's BIOL 2050 class, went to Danby Woods to do group research. The class was split into groups of 4, except for my group which consisted of 5 students: myself, Antoni, Taylor, Hinal, and Amena. The five of us decided to collect insect abundance and disversity in the grassland and woodlot regions of Danby Woods using qudrats. We also wanted to see whether moisture in the soil would affect the number of insects we saw in certain regions of the woods. We planned on taking 60 quadrats, for a total of 120, per day and looking at each quadrat for 5 minutes. We used the random numbers website to determine how many steps we should take to reach our next experimental site. Four of us took four quadrats, doing 7-8 procedures each, while one person took the moisture meter and measured the moisture of each of our quadrats. On day 1, the weather was approximately 14 degrees Celsius and partly cloudy, with a wind speed of 26 km per hour and a humidity of 49%. This day not as many insects were witnessed, both in high moisture and low moisture levels. This could be because of the weather that day, it was extremely windy and somewhat cold. On day 2, the weather was approximately 9 degrees Celsius, with a wind speed of 14 km per hour and 57% humidity. On this day, it was also evident that moisture did not play a critical role in the abundance and diversity of insects, as the moisture levels did not correlate with an increase or decrease in insects. Again, the weather was chilly that day, but it was not as windy as it was on day 1. Although, it is certain that the woodlot had much less diversity and abundance than the grassland, but because the data was not consistent it is difficult to connect.
DOI: 10.6084/m9.figshare.1590962.v4
title: A Textual-based Technique for Code and Test Smell Detection

description: Bad smells are symptoms of poor design or imple- mentation choices that may affect both production and test code. Previous studies demonstrated the huge impact of these smells on change- and fault-proneness, comprehensibility and, more in general, on maintainability. For these reasons researchers have spent a lot of effort on the definition of methods and tools aimed at detecting smells in production and test code. Existing detection techniques are largely based on using structural information extracted from source code without considering the textual content of the code, which can be a useful source of information to detect design flaws. In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect smells in both production code and test code. We run TACO on 20 open source projects, comparing its performance with existing state-of-the-art smell detectors purely based on structural analysis. Our findings indicate that TACO’s precision ranges between 57% and 75%, while its recall ranges between 68% and 85%. Moreover, we observed that TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of source code components.
DOI: 10.6084/m9.figshare.1590037.v1
title: The Diversity and Abundance of Insects in Danby Woods Based on the Moisture Levels of the Soil

description: On October 19 and October 26, 2015, ths students from Bailey Hewitt's laboratory 01, in Christopher Lortie's BIOL 2050 class, went to Danby Woods to do group research. The class was split into groups of 4, except for my group which consisted of 5 students: myself, Antoni, Taylor, Hinal, and Amena. The five of us decided to collect insect abundance and disversity in the grassland and woodlot regions of Danby Woods using qudrats. We also wanted to see whether moisture in the soil would affect the number of insects we saw in certain regions of the woods. We planned on taking 60 quadrats, for a total of 120, per day and looking at each quadrat for 5 minutes. We used the random numbers website to determine how many steps we should take to reach our next experimental site. Four of us took four quadrats, doing 7-8 procedures each, while one person took the moisture meter and measured the moisture of each of our quadrats. On day 1, the weather was approximately 14 degrees Celsius and partly cloudy, with a wind speed of 26 km per hour and a humidity of 49%. This day not as many insects were witnessed, both in high moisture and low moisture levels. This could be because of the weather that day, it was extremely windy and somewhat cold. On day 2, the weather was approximately 9 degrees Celsius, with a wind speed of 14 km per hour and 57% humidity. On this day, it was also evident that moisture did not play a critical role in the abundance and diversity of insects, as the moisture levels did not correlate with an increase or decrease in insects. Again, the weather was chilly that day, but it was not as windy as it was on day 1. Although, it is certain that the woodlot had much less diversity and abundance than the grassland, but because the data was not consistent it is difficult to connect.
DOI: 10.6084/m9.figshare.1590036
title: Roll Call Votes: United States House and Senate

description: In using this data, please be sure to cite (1) http://voteview.com/ [source of original data; processed version distributed with permission of collators of original data] (2) "Party polarization in Congress: a network science approach," A. Waugh, L. Pei, J. H. Fowler, P. J. Mucha and M. A. Porter, arXiv:0907.3509. [paper to cite for our processed version of the Voteview data] Newly reprocessed data: Raw data taken from voteview (voteview.tar.gz or 1-108, and get final 109 and 110 from voteview site).<br><br>The .zip file also includes Matlab code.
DOI: 10.6084/m9.figshare.1590962.v5
title: A Textual-based Technique for Smell Detection

description: In this paper, we present TACO (Textual Analysis for Code Smell Detection), a technique that exploits textual analysis to detect a family of smells of different nature and different levels of granularity. We run TACO on 10 open source projects, comparing its performance with existing smell detectors purely based on structural information extracted from code components. The analysis of the results indicate that TACO’s precision ranges between 67% and 77%, while its recall ranges between 72% and 84%. Also, TACO often outperforms alternative structural approaches confirming, once again, the usefulness of information that can be derived from the textual part of code components.
DOI: 10.6084/m9.figshare.1590032
title: Direct Detection of unnatural DNA nucleotides dNaM and d5SICS with the MspA nanopore

description: A set of matlab files containing the consensus plots, and events used to create them as in Craig et al. 2015. Feel free to email about any questions.
DOI: 10.6084/m9.figshare.1590034
title: Canine CT sternal lymph node data

description: Canine sternal lymph node CT appearance data annotated to patient disease data
DOI: 10.6084/m9.figshare.1590034.v1
title: Canine CT sternal lymph node data

description: Canine sternal lymph node CT appearance data annotated to patient disease data
DOI: 10.6084/m9.figshare.1590026
title: Computer Experiments With Both Qualitative and Quantitative Variables

description: Computer experiments have received a great deal of attention in many fields of science and technology. Most literature assumes that all the input variables are quantitative. However, researchers often encounter computer experiments involving both qualitative and quantitative variables (BQQV). In this article, a new interface on design and analysis for computer experiments with BQQV is proposed. The new designs are one kind of sliced Latin hypercube designs with points clustered in the design region and possess good uniformity for each slice. For computer experiments with BQQV, such designs help to measure the similarities among responses of different level-combinations in the qualitative variables. An adaptive analysis strategy intended for the proposed designs is developed. The proposed strategy allows us to automatically extract information from useful auxiliary responses to increase the precision of prediction for the target response. The interface between the proposed design and the analysis strategy is demonstrated to be effective via simulation and a real-life example from the food engineering literature. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1590032.v2
title: Direct Detection of unnatural DNA nucleotides dNaM and d5SICS with the MspA nanopore

description: A set of matlab files containing the consensus plots, and events used to create them as in Craig et al. 2015. Feel free to email about any questions.
DOI: 10.6084/m9.figshare.1590032.v1
title: Direct Detection of unnatural DNA nucleotides dNaM and d5SICS with the MspA nanopore

description: A set of matlab files containing the consensus plots, and events used to create them as in Craig et al. 2015. Feel free to email about any questions.
DOI: 10.6084/m9.figshare.1590026.v1
title: Computer Experiments With Both Qualitative and Quantitative Variables

description: Computer experiments have received a great deal of attention in many fields of science and technology. Most literature assumes that all the input variables are quantitative. However, researchers often encounter computer experiments involving both qualitative and quantitative variables (BQQV). In this paper, a new interface on design and analysis for computer experiments with BQQV is proposed. The new designs are one kind of sliced Latin hypercube designs with points clustered in the design region, and possess good uniformity for each slice. For computer experiments with BQQV, such designs help to measure the similarities among responses of different level-combinations in the qualitative variables. An adaptive analysis strategy intended for the proposed designs is developed. The proposed strategy allows us to automatically extract information from useful auxiliary responses to increase the precision of prediction for the target response. The interface between the proposed design and the analysis strategy is demonstrated to be effective via simulation and a real-life example from the food engineering literature. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1590017
title: Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences

description: Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, nonstationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a nonstationary Gaussian process model across the clusters, which allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1590017.v2
title: Nonstationary Gaussian Process Models Using Spatial Hierarchical Clustering from Finite Differences

description: Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, nonstationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a nonstationary Gaussian process model across the clusters, which allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1590018.v2
title: Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition

description: In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the article.
DOI: 10.6084/m9.figshare.1590009
title: Presentation on Making Plots and Diagrams for Presentations, Papers, and Theses

description: Due to file naming issues in this share, it is replaced by a new one at http://dx.doi.org/10.6084/m9.figshare.1590982 (also in the link at the bottom). The new one has these two versions (0.1 and 0.1.1) and all newer versions. Presentation on making plots and diagrams for presentations, papers, and theses. The primary audience, at the present time, is undergraduate and graduate students in STEM fields. The source for the presentation, its diagrams, and the Jupyter/IPython notebook to make the plots are at the provided GitHub link and are licensed CC BY 4.0. The abstract is below Plots and diagrams are a common visual means of conveying information in scientific talks, papers, and theses. A good plot or diagram conveys the desired information clearly and in a way that gives understanding that would be lacking in a table or description giving the same information by itself. In addition, the plot or diagram should be understandable to people with various types of colorblindness, when printed on a printer (color or greyscale), or shown on a projector. A bad plot or diagram can serve as a distraction and cause confusion, or be otherwise difficult to read. This presentation will show how to make plots and diagrams more clear and readable. In addition, computer and printing considerations will be discussed. These include screens and projectors, printers, image formats, fonts, and available software for plotting, making diagrams, and making presentations
DOI: 10.6084/m9.figshare.1590012
title: Woodcreeper attacks Polybia scrobalis nest

description: An unidentified woodcreeper (perhaps<em> Dedrocincla merula</em>) attacks a nest of <em>Polybia scrobalis</em> at the Nouragues Station, French Guiana. This attack commenced at 7:44 am local time, on March 5, 2010
DOI: 10.6084/m9.figshare.1590017.v1
title: Nonstationary Gaussian Process Models using Spatial Hierarchical Clustering from Finite Differences

description: Modern digital data production methods, such as computer simulation and remote sensing, have vastly increased the size and complexity of data collected over spatial domains. Analysis of these large spatial datasets for scientific inquiry is typically carried out using the Gaussian process. However, non-stationary behavior and computational requirements for large spatial datasets can prohibit efficient implementation of Gaussian process models. To perform computationally feasible inference for large spatial data, we consider partitioning a spatial region into disjoint sets using hierarchical clustering of observations and finite differences as a measure of dissimilarity. Intuitively, directions with large finite differences indicate directions of rapid increase or decrease and are, therefore, appropriate for partitioning the spatial region. Spatial contiguity of the resulting clusters is enforced by only clustering Voronoi neighbors. Following spatial clustering, we propose a non-stationary Gaussian process model across the clusters that allows the computational burden of model fitting to be distributed across multiple cores and nodes. The methodology is primarily motivated and illustrated by an application to the validation of digital temperature data over the city of Houston as well as simulated datasets.
DOI: 10.6084/m9.figshare.1590018.v1
title: Anomaly Detection in Images with Smooth Background Via Smooth-Sparse Decomposition

description: In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this paper, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smoothsparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This paper has supplementary materials that includes all the technical details, proofs, MATLAB codes and simulated images used in the paper.
DOI: 10.6084/m9.figshare.1590012.v1
title: Woodcreeper attacks Polybia scrobalis nest

description: An unidentified woodcreeper (perhaps<em> Dedrocincla merula</em>) attacks a nest of <em>Polybia scrobalis</em> at the Nouragues Station, French Guiana. This attack commenced at 7:44 am local time, on March 5, 2010
DOI: 10.6084/m9.figshare.1589818
title: Spades E coli assembly visualised with bandage

description: While preparing for a course on de novo genome assembly, I tried out the excellent Bandage program for viewing assembly graphs. One spades assembly stuck out, as it showed a 'knot' of short contigs. Inspection of the sequencing of these contigs showed them to be derived from the rRNA loci of the genome (rRNA and tRNA genes). After I posted an image of the graph on twitter, a fellow researcher asked me for a citable copy of the figure. This figshare entry is meant to provide just that. I've also included the fastg file so those interested can load it into bandage.
DOI: 10.6084/m9.figshare.1590009.v1
title: Presentation on Making Plots and Diagrams for Presentations, Papers, and Theses

description: Presentation on making plots and diagrams for presentations, papers, and theses. The primary audience, at the present time, is undergraduate and graduate students in STEM fields. The source for the presentation, its diagrams, and the Jupyter/IPython notebook to make the plots are at the provided GitHub link and are licensed CC BY 4.0. The abstract is below Plots and diagrams are a common visual means of conveying information in scientific talks, papers, and theses. A good plot or diagram conveys the desired information clearly and in a way that gives understanding that would be lacking in a table or description giving the same information by itself. In addition, the plot or diagram should be understandable to people with various types of colorblindness, when printed on a printer (color or greyscale), or shown on a projector. A bad plot or diagram can serve as a distraction and cause confusion, or be otherwise difficult to read. This presentation will show how to make plots and diagrams more clear and readable. In addition, computer and printing considerations will be discussed. These include screens and projectors, printers, image formats, fonts, and available software for plotting, making diagrams, and making presentations