Data for: Extracting Phylogenetic Signal from Phylogenomic Data: Higher-Level Relationships of the Nightbirds (Strisores)
Contributors: Noor White, Michael Braun
... The data associated with the article "Extracting Phylogenetic Signal from Phylogenomic Data: Higher-Level Relationships of the Nightbirds (Strisores)" published in 2019 in Molecular Phylogenetics and Evolution by Noor D. White and Michael J. Braun includes the following files: Matrices_phylip.tar.gz: Concatenated nucleotide matrices used in this study, including character sets detailing individual UCE loci Tree files are those found in Figure 5 of the manuscript. They include: Trees_concatenated_ACGT.tar.gz: Results of the concatenated analysis of nucleotide matrices Trees_concatenated_RY.tar.gz: Results of the concatenated analysis of RY-coded matrices Trees_gene_tree_sum_ACGT.tar.gz: Results of the gene tree summarization of nucleotide matrices Trees_gene_tree_sum_RY.tar.gz: Results of the gene tree summarization of RY-coded matrices Trees_Low_GC_Variance.tar.gz: Results of the concatenated analysis of nucleotide matrices containing only UCE loci with low GC variance (10% highest GC variant loci removed) Trees_Part_by_Locus.tar.gz: Results of the concatenated analysis of nucleotide matrices partitioned by UCE locus
Contributors: Md. Asifuzzaman Jishan, Khan Raqib Mahmud, Abul Kalam Al Azad
... We represented a new Bangla dataset with a Hybrid Recurrent Neural Network model which generated Bangla natural language description of images. This dataset achieved by a large number of images with classification and containing natural language process of images. We conducted experiments on our self-made Bangla Natural Language Image to Text (BNLIT) dataset. Our dataset contained 8,743 images. We made this dataset using Bangladesh perspective images. We used one annotation for each image. In our repository, we added two types of pre-processed data which is 224 × 224 and 500 × 375 respectively alongside annotations of full dataset. We also added CNN features file of whole dataset in our repository which is features.pkl.
Contributors: Andreza Cristina Beezao Moreira
... Results of the computational experiments on the set of 1440 instances named IPMTC-II, used in the paper "Scheduling identical parallel machines with tooling constraints", by Beezão, Cordeau, Laporte and Yanasse, published at EJOR, 2017 (https://www.sciencedirect.com/science/article/abs/pii/S0377221716306233) For further questions, please contact me at: andreza.beezao@gmail or email@example.com.
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Contributors: Andreza Cristina Beezao Moreira
... Set of 2880 instances named IPMTC-I and IPMTC-II, and used in the paper "Scheduling identical parallel machines with tooling constraints", by Beezão, Cordeau, Laporte and Yanasse, published at EJOR, 2017 (https://www.sciencedirect.com/science/article/abs/pii/S0377221716306233) For further questions, please contact me at: andreza.beezao@gmail or firstname.lastname@example.org.
Contributors: Nihat ONER, Hakan Gultekin, Çağrı Koç
... These data tests contain benchmark instances for airport shuttle scheduling problem which includes several difficult decisions such as splitting the jobs, grouping the jobs, and scheduling the vehicles and is introduced in the paper titled "The Airport Shuttle Service Scheduling Problem". In the ASSSP, we transfer passengers from or to the airport and city center. There are a number of identical vehicles used for the transfer of passengers. A certain number of passengers of each flight that arrives to the airport requests a transfer to the city center. Similarly, a certain number of the passengers of each flight that will depart from the airport requests transfer service. The vehicle capacity is limited and it is renewed at the airport or at the city center. The passengers who are ready to transfer at the same time with shuttle are called as jobs. Each job has a demand. The demand is divisible. Customers reserve their seats at least one day before their flights. The transfer company is private which seeks profit. Therefore, it is allowed to reject the transportation of some customers with no extra cost. The flight schedule and the number of transfer passengers of each flight are known. The problem is to determine the assignment of passengers to the vehicles and the transfer starting times of vehicles. The objective of the ASSSP is to maximize the profit which is the revenues earned from each transferred customer less the empty and loaded transfer costs. You can find the details about parameters used in the instances in "explanation.txt" file. If you find any errors, want to comment, have any suggestions or have any additional benchmark instances, you are welcomed to share and write an e-mail to email@example.com.
Contributors: marco mendoza, Maria B. Goncalves, Jonathan Corcoran
... Differential gene expression analysis (RNA-sequencing) assessed in the SC from L5-SNL rats and non-injured rats, treated with vehicle or C286. Raw counts per gene (Raw counts.zip file) were normalized (quantile normalization; Normaliez counts.zip), then used for a differential gene expression analysis relative to the sample non-injured + vehicle (Diff_expression_DESeq2.zip). In addition, we have included herein a supplementary Table containing Gene Ontology (GO) terms per gene co-expression paths issued from the differential expression analysis. Notice that GO terms are described by their confidence of enrichment, expressend on "-10*log10(p-value).
Contributors: Willem Roux, Imtiaz Gandikota, Guilian Yi
... These are the computer models used for structural topology optimization from the paper "A spatial kernel approach for topology optimization" by Roux, Yi, and Gandikota.
Contributors: Jeremy Diem
... These data were generated for an analysis of trends in satellite-based seasonal rainfall in western Uganda from 1983-2017. Western Uganda was divided into five rainfall regions. Analyses of the data show the following: (1) wet days in most seasons have a disproportionately high frequency of westerly back-trajectories extending over the Congo Basin; (2) rainy seasons, especially the first rains, have gotten longer and wetter throughout western Uganda; (3) rainfall also has increased for climatological seasons, with the exception being December-February; and (4) increases in middle-troposphere specific humidity and vertical ascent over time provide support for the wetting trends derived from the satellite-derived rainfall data.
Data for: Experimental characterization and hyperelastic constitutive modeling of open-cell elastomeric foams
Contributors: Alexander Landauer, Xiuqi Li
... Various experimental and model datasets for capturing the quasi-static constitutive response of three densities of Poron XRD (Rogers Corp, Rogers, CT) elastomeric polymer foam. Uniaxial, multi-modal, and inhomogeneous loading modes are included. For more details on data collection and post-processing see the relevant sections of the full paper. For additional details please contact the authors directly or via GitHub.
Data for: Classification methods for point clouds in rock slope monitoring: a novel machine learning approach and comparative analysis
Contributors: Luke Weidner, Gabriel Walton, Ryan Kromer
... Data for the following submission: Title: Classification methods for point clouds in rock slope monitoring: a novel machine learning approach and comparative analysis Weidner, L.1*, Walton, G.1, Kromer, R.1 1Colorado School of Mines, Golden, USA *Corresponding author email: firstname.lastname@example.org In the event the manuscript is unavailable, please reach out to us for a copy. The main contents of this file are as follows: -Supplementary figures referenced in the manuscript -All processed point clouds used in the through-time analysis. (~9.3 GB) -Scripts used to calculate the results shown in Figures 11, 12 and 13. (~1.6 GB) -Numeric data in other tables, graphs, and figures. Due to the nature of the research, many large point clouds are created, too many to be all uploaded to this repository. If you are looking for data that is not provided in this dataset, please reach out to the authors and we would be happy to provide any additional data. Scripts labeled "RUNME" are found in the main file directory for creating the ML method results ('tests_RUNME.m'), and for hybrid and masking results. For the most part, scripts can be run without modification and should provide results (assuming the required MATLAB toolboxes are installed) Note that for hybrid and masking, multiple runs of the script are required, changing the filenames at the beginning of the script for each of the four dates calculated. The Random Forest TreeBagger object ('tb_t14_jun16dec18') is also included and all the feature sets used for training and validation ('date_struct.mat').