Displaying datasets 276 - 300 of 456 in total

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published: 2019-10-27
 
This dataset accompanies the paper "STREETS: A Novel Camera Network Dataset for Traffic Flow" at Neural Information Processing Systems (NeurIPS) 2019. Included are: *Over four million still images form publicly accessible cameras in Lake County, IL. The images were collected across 2.5 months in 2018 and 2019. *Directed graphs describing the camera network structure in two communities in Lake County. *Documented non-recurring traffic incidents in Lake County coinciding with the 2018 data. *Traffic counts for each day of images in the dataset. These counts track the volume of traffic in each community. *Other annotations and files useful for computer vision systems. Refer to the accompanying "readme.txt" or "readme.pdf" for further details.
keywords: camera network; suburban vehicular traffic; roadways; computer vision
published: 2019-10-16
 
Human annotations of randomly selected judged documents from the AP 88-89, Robust 2004, WT10g, and GOV2 TREC collections. Seven annotators were asked to read documents in their entirety and then select up to ten terms they felt best represented the main topic(s) of the document. Terms were chosen from among a set sampled from the document in question and from related documents.
keywords: TREC; information retrieval; document topicality; document description
published: 2019-10-05
 
This dataset contains collected and aggregated network information from NCSA’s Blue Waters system, which is comprised of 27,648 nodes connected via Cray Gemini* 3D torus (dimension 24x24x24) interconnect, from Jan/01/2017 to May/31/2017. Network performance counters for links are exposed via Cray's gpcdr (<a href="https://github.com/ovis-hpc/ovis/wiki/gpcdr-kernel-module">https://github.com/ovis-hpc/ovis/wiki/gpcdr-kernel-module</a>) kernel module. Lightweight Distributed Metric Service ([LDMS](<a href="https://github.com/ovis-hpc/ovis">https://github.com/ovis-hpc/ovis</a>)) is used to sampled the performance counters at 60 second intervals. Please read "README.md" file. <b>Acknowledgement:</b> This dataset is collected as a part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.
keywords: HPC; Interconnect; Network; Congestion; Blue Waters; Dataset
published: 2019-11-12
 
We are sharing the tweet IDs of four social movements: #BlackLivesMatter, #WhiteLivesMatter, #AllLivesMatter, and #BlueLivesMatter movements. The tweets are collected between May 1st, 2015 and May 30, 2017. We eliminated the location to the United States and focused on extracting the original tweets, excluding the retweets. Recommended citations for the data: Rezapour, R. (2019). Data for: How do Moral Values Differ in Tweets on Social Movements?. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9614170_V1 and Rezapour, R., Ferronato, P., and Diesner, J. (2019). How do moral values differ in tweets on social movements?. In 2019 Computer Supported Cooperative Work and Social Computing Companion Publication (CSCW’19 Companion), Austin, TX.
keywords: Twitter; social movements; black lives matter; blue lives matter; all lives matter; white lives matter
published: 2019-10-23
 
Raw MD simulation trajectory, input and configuration files, SEM current data, and experimental raw data accompanying the publication, "Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore". README.md contains a description of all associated files.
keywords: molecular dynamics; protein sequencing; aerolysin; nanopore sequencing
published: 2019-10-19
 
Large, distributed microphone arrays could offer dramatic advantages for audio source separation, spatial audio capture, and human and machine listening applications. This dataset contains acoustic measurements and speech recordings from 10 loudspeakers and 160 microphones spread throughout a large, reverberant conference room. The distributed microphone system contains two types of array: four wearable microphone arrays of 16 sensors each placed near the ears and across the upper body, and twelve tabletop arrays of 8 microphones each in enclosures designed to resemble voice-assistant speakers. The dataset includes recordings of chirps that can be used to measure impulse responses and of speech clips derived from the CSTR VCTK corpus. The speech clips are recorded both individually and as a mixture to support source separation experiments. The uncompressed files are about 13.4 GB.
keywords: microphone arrays; audio source separation; augmented listening; wireless sensor networks
published: 2019-10-18
 
Supporting secondary data used in a manuscript currently in submission regarding the invasion dynamics of the asian tiger mosquito, Aedes albopictus, in the state of Illinois
keywords: albopictus;mosquito
published: 2019-10-15
 
Filtered trophallaxis interactions for two honeybee colonies, each containing 800 worker bees and one queen. Each colony consists of bees that were administered a juvenile hormone analogy, a vehicle treatment, or a sham treatment to determine the effect of colony perturbation on the duration of trophallaxis interactions. Columns one and two display the unique identifiers for each bee involved in a particular trophallaxis exchange, and columns three and four display the Unix timestamp of the beginning/end of the interaction (in milliseconds), respectively.<br /><b>Note</b>: the queen interactions were omitted from the uploaded dataset for reasons that are described in submitted manuscript. Those bees that performed poorly are also omitted from the final dataset.
keywords: honey bee; trophallaxis; social network
published: 2019-10-03
 
Dataset for F2F events of honeybees. F2F events are defined as face-to-face encounters of two honeybees that are close in distance and facing each other but not connected by the proboscis, thus not engaging in trophallaxis. The first and the second columns show the unique id's of honeybees participating in F2F events. The third column shows the time at which the F2F event started while the fourth column shows the time at which it ended. Each time is in the Unix epoch timestamp in milliseconds.
keywords: honeybee;face-to-face interaction
published: 2019-07-04
 
Software (Matlab .m files) for the article: Lying in Wait: Modeling the Control of Bacterial Infections via Antibiotic-Induced Proviruses. The files can be used to reproduce the analysis and figures in the article.
keywords: Matlab codes; antibiotic-induced dynamics
published: 2019-09-01
 
Agriculture has substantial socioeconomic and environmental impacts that vary between crops. However, information on how the spatial distribution of specific crops has changed over time across the globe is relatively sparse. We introduce the Probabilistic Cropland Allocation Model (PCAM), a novel algorithm to estimate where specific crops have likely been grown over time. Specifically, PCAM downscales annual and national-scale data on the crop-specific area harvested of 17 major crops to a global 0.5-degree grid from 1961-2014. The resulting database presented here provides annual global gridded likelihood estimates of crop-specific areas. Both mean and standard deviations of grid cell fractions are available for each of the 17 crops. Each netCDF file contains an individual year of data with an additional variable ("crs") that defines the coordinate reference system used. Our results provide new insights into the likely changes in the spatial distribution of major crops over the past half-century. For additional information, please see the related paper by Jackson et al. (2019) in Environmental Research Letters (https://doi.org/10.1088/1748-9326/ab3b93).
keywords: global; gridded; probabilistic allocation; crop suitability; agricultural geography; time series
published: 2019-09-25
 
<sup>12</sup>CO and <sup>13</sup>CO maps for six molecular clouds in the Large Magellanic Cloud, obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). See the associated article in the Astrophysical Journal, and README files within each ZIP archive. Please cite the article if you use these data.
keywords: Radio astronomy
published: 2019-09-17
 
BAM files for evolved strains from migration rate selection experiments conducted in low viscosity (0.2% w/v) agar plates containing M63 minimal medium with 1mM of mannose, melibiose, N-acetylglucosamine or galactose
published: 2019-09-17
 
Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification_tagging.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
keywords: twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning; classification; sequence tagging
published: 2019-09-17
 
Trained models for multi-task multi-dataset learning for text classification in tweets. Classification tasks include sentiment prediction, abusive content, sarcasm, and veridictality. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_classification.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
keywords: twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning; sentiment; sarcasm; abusive content;
published: 2019-09-17
 
Trained models for multi-task multi-dataset learning for sequence tagging in tweets. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929
keywords: twitter; deep learning; machine learning; trained models; multi-task learning; multi-dataset learning;
published: 2019-09-06
 
This is a dataset of 1101 comments from The New York Times (May 1, 2015-August 31, 2015) that contains a mention of the stemmed words vaccine or vaxx.
keywords: vaccine;online comments
published: 2019-09-05
 
The data set here include data from NMR, LC-MS/MS, MALDI-MS, H/D exchange MS experiments used in paper "A novel rotifer derived alkaloid paralyzes schistosome larvae and prevents infection".
published: 2019-08-29
 
This is the published ortholog set derived from whole genome data used for the analysis of members of the B. tabaci complex of whiteflies. It includes the concatenated alignment and individual gene alignments used for analyses (Link to publication: https://www.mdpi.com/1424-2818/11/9/151).
published: 2019-07-04
 
Results generated using SharpTNI on data collected from the 2014 Ebola outbreak in Sierra Leone.
published: 2019-07-29
 
Datasets used in the study, "TRACTION: Fast non-parametric improvement of estimated gene trees," accepted at the Workshop on Algorithms in Bioinformatics (WABI) 2019.
keywords: Gene tree correction; horizontal gene transfer; incomplete lineage sorting
published: 2019-08-15
 
Simulation data related to the paper "Mastitis risk effect on the economic consequences of paratuberculosis control in dairy cattle: A stochastic modeling study"
keywords: paratuberculosis;simulation;dairy
published: 2019-08-13
 
Multiple sequence alignments from concatenated nuclear and mitochondrial genes and resulting phylogenetic tree files of fruit doves and their close relatives. Files include: BEAST input XML file (fruit_dove_beast_input.xml); a maximum clade credibility tree from a BEAST analysis (fruit_dove_beast_mcc.tre); concatenated multiple sequence alignment NEXUS files for the novel dataset (fruit_dove_concatenated_alignment.nex, 76 taxa, 4,277 characters) and the dataset with additional sequences (fruit_dove_plus_cibois_data_concatenated_alignment.nex, 204 taxa, 4,277 characters), both of which contain a MrBayes block including partition information; and 50% majority-rule consensus trees generated from MrBayes analyses, using the NEXUS alignment files as inputs (fruit_dove_mrbayes_consensus.tre, fruit_dove_plus_cibois_data_mrbayes_consensus.tre).
keywords: fruit doves; multiple sequence alignment; phylogeny; Aves: Columbidae
published: 2019-08-30
 
This dataset includes the data from an analysis of bobcat harvest data with particular focus on the relationship between catch-per-unit-effort and population size. The data relate to bobcat trapper and hunter harvest metrics from Wisconsin and include two RDS files which can be open in the software R using the readRDS() function.
keywords: bobcat; catch-per-unit-effort; CPUE; harvest; Lynx rufus; wildlife management; trapper; hunter
published: 2019-08-05
 
The data in this directory corresponds to: Skinner, R.K., Dietrich, C.H., Walden, K.K.O., Gordon, E., Sweet, A.D., Podsiadlowski, L., Petersen, M., Simon, C., Takiya, D.M., and Johnson, K.P. Phylogenomics of Auchenorrhyncha (Insecta: Hemiptera) using Transcriptomes: Examining Controversial Relationships via Degeneracy Coding and Interrogation of Gene Conflict. Systematic Entomology. Correspondance should be directed to: Rachel K. Skinner, rskinn2@illinois.edu If you use these data, please cite our paper in Systematic Entomology. The following files can be found in this dataset: Amino_acid_concatenated_alignment.phy: the amino acid alignment used in this analysis in phylip format. Amino_acid_raxml_partitions.txt (for reference only): the partitions for the amino acid alignment, but a partitioned amino acid analysis was not performed in this study. Amino_acid_concatenated_tree.newick: the best maximum likelihood tree with bootstrap values in newick format. ASTRAL_input_gene_trees.tre: the concatenated gene tree input file for ASTRAL README_pie_charts.md: explains the the scripts and data needed to recreate the pie charts figure from our paper. There is also another Corresponds to the following files: ASTRAL_species_tree_EN_only.newick: the species tree with only effective number (EN) annotation ASTRAL_species_tree_pp1_only.newick: the species tree with only the posterior probability 1 (main topology) annotation ASTRAL_species_tree_q1_only.newick: the species tree with only the quartet scores for the main topology (q1) ASTRAL_species_tree_q2_only.newick: the species tree with only the quartet scores for the first alternative topology (q2) ASTRAL_species_tree_q3_only.newick: the species tree with only the quartet scores for the second alternative topology (q3) print_node_key_files.py: script needed to create the following files: node_keys.key: text file with node IDs and topologies complete_q_scores.key: text file with node IDs multiplied q scores EN_node_vals.key: text file with node IDs and EN values create_pie_charts_tree.py: script needed to visualize the tree with pie charts, pp1, and EN values plotted at nodes ASTRAL_species_tree_full_annotation.newick: the species tree with full annotation from the ASTRAL analysis. NOTE: It may be more useful to examine individual value files if you want to visualize the tree, e.g., in figtree, since the full annotations are extensive and can make viewing difficult. Complete_NT_concatenated_alignment.phy: the nucleotide alignment that includes unmodified third codon positions. The alignment is in phylip format. Complete_NT_raxml_partitions.txt: the raxml-style partition file of the nucleotide partitions Complete_NT_concatenated_tree.newick: the best maximum likelihood tree from the concatenated complete analysis NT with bootstrap values in newick format Complete_NT_partitioned_tree.newick: the best maximum likelihood tree from the partitioned complete NT analysis with bootstrap values in newick format Degeneracy_coded_nt_concatenated_alignment.phy: the degeneracy coded nucleotide alignment in phylip format Degeneracy_coded_nt_raxml_partitions.txt: the raxml-style partition file for the degeneracy coded nucleotide alignment Degeneracy_coded_nt_concatenated_tree.newick: the best maximum likelihood tree from the degeneracy-coded concatenated analysis with bootstrap values in newick format Degeneracy_coded_nt_partitioned_tree.newick: the best maximum likelihood tree from the degeneracy-coded partitioned analysis with bootstrap values in newick format count_ingroup_taxa.py: script that counts the number of ingroup and/or outgroup taxa present in an alignment
keywords: Auchenorrhyncha; Hemiptera; alignment; trees