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Displaying datasets 401 - 425 of 584 in total
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published: 2019-12-17
Zhang, Yujie; Araiza Bravo, Rodrigo; Chitambar, Eric; Lorenz, Virginia (2019): Dataset for "Channel Activation of CHSH Nonlocality". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3686727_V1
This dataset provides the raw data, code and related figures for the paper, "Channel Activation of CHSH Nonlocality"
keywords:
Super-activation; Non-locality breaking channel
published: 2019-12-12
Kamuda, Mark; Huff, Kathryn (2019): Automated Isotope Identification and Quantification Using Artificial Neural Networks. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4860767_V1
This dataset contains gamma-ray spectra templates for a source interdiction and uranium enrichment measurement task. This dataset also contains Keras machine learning models trained using datasets created using these templates.
keywords:
gamma-ray spectroscopy; neural networks; machine learning; isotope identification; uranium enrichment; sodium iodide; NaI(Tl)
published: 2019-12-10
Yang, Pan; Zhao, Qiankun; Cai, Ximing (2019): Land productivity and land availability for growing bioenergy crop in the Contiguous US. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4584681_V1
The dataset consists of two types of data: the estimate of land productivity (the maximum productivity, MP) and the estimate of land that has low productivity for any major crops planted in the Contiguous United States and then may be available for growing bioenergy crops (the marginal land, ML). All data items are in GeoTiff format, under the World Geodetic System (WGS) 84 project, and with a resolution of 0.0020810045 degree (~250 m). The MP values are calculated based on machine learning model estimated yields of major crops in the CONUS, and its expected value (MP_mean.tif), and associated uncertainty (MP_IDP.tif). The ML availability data have two versions: a deterministic version and a version with uncertainty. The deterministic MLs are determined as the land pixels with expected MP values falling in the range defined in the following criteria, and the MLs with uncertainty are determined as the probability that the MP value of a land pixel falls in the range defined in the following criteria: Criteria_____Description S1________ Current crop and pasture land with MP <= P50 S2________ Current crop and pasture land with MP <= P25 S3________ S1 + current grass and shrub land with P25 < MP < P50 S4________ S2 + current grass and shrub land with P10 < MP < P25 Economic__ Current crop and pasture land with potential profitability < 0 Here P10, P25 and P50 are the 10th, 25th and 50th percentile of crop MP values
keywords:
Land productivity;marginal land;land use
published: 2019-12-03
de Moya, Robert (2019): Feather Louse Orthology set. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0440388_V1
This is the data set associated with the manuscript titled "Extensive host-switching of avian feather lice following the Cretaceous-Paleogene mass extinction event." Included are the gene alignments used for phylogenetic analyses and the cophylogenetic input files.
keywords:
phylogenomics, cophylogenetics, feather lice, birds
published: 2019-11-18
Zhang, Chuanyi; Ochoa, Idoia (2019): VCF files used for VEF: a Variant Filtering tool based on Ensemble methods. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9401259_V1
VCF files used to analyze a novel filtering tool VEF, presented in the article "VEF: a Variant Filtering tool based on Ensemble methods".
keywords:
VCF files; filtering; VEF
published: 2019-10-27
Snyder, Corey; Do, Minh (2019): Data for STREETS: A Novel Camera Network Dataset for Traffic Flow. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3671567_V1
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
Sherman, Garrick (2019): TREC document topic annotations. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9822674_V1
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
Saurabh, Jha; Archit, Patke; Mike, Showerman; Jeremy, Enos; Greg, Bauer; Zbigniew, Kalbarczyk; Ravishankar, Iyer; William , Kramer (2019): Monet - Blue Waters Network Dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2921318_V1
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
Rezapour, Rezvaneh (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
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: 2019-10-19
Corey, Ryan M.; Skarha, Matthew D.; Singer, Andrew C. (2019): Massive Distributed Microphone Array Dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6216881_V1
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
Smith, Rebecca (2019): Spatial and Temporal Invasion Dynamics of Aedes albopictus (Diptera: Culicidae) in Illinois. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7540359_V1
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
Choi, Sang Hyun; Rao, Vikyath; Gernat, Tim; Hamilton, Adam; Robinson, Gene; Goldenfeld, Nigel (2019): Honeybee trophallaxis event data for The origin of heavy tails in honeybee and human interaction times. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2712449_V1
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
Choi, Sang Hyun; Rao, Vikyath D.; Gernat, Tim; Hamilton, Adam R.; Robinson, Gene E.; Goldenfeld, Nigel (2019): Honeybee F2F event data for The origin of heavy tails in honeybee and human interaction times. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4021786_V1
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
Rapti, Zoi (2019): Control of bacterial infections via antibiotic-induced proviruses . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9721455_V1
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
Jackson, Nicole; Konar, Megan; Debaere, Peter; Estes, Lyndon (2019): Data for: Probabilistic global maps of crop-specific areas from 1961 to 2014. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7439710_V1
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
Wong, Tony; Hughes, A; Tokuda, K; Indebetouw, R; Onishi, T; Bandurski, J. B.; Chen, C. H. R.; Fukui, Y; Glover, S. C. O.; Klessen, R. S.; Pineda, J. L.; Roman-Duval, J.; Sewilo, M.; Wojciechowski, E.; Zahorecz, S. (2019): Data for: Relations Between Molecular Cloud Structure Sizes and Line Widths in the Large Magellanic Cloud. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7090706_V1
<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
Fraebel, David T.; Kuehn, Seppe (2019): Sequencing data for migration rate selection experiments (0.2% agar, 1mM sugar). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2128477_V1
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
Mishra, Shubhanshu (2019): Trained models for multi-task multi-dataset learning for text classification as well as sequence tagging in tweets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1094364_V1
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
Mishra, Shubhanshu (2019): Trained models for multi-task multi-dataset learning for text classification in tweets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1917934_V1
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
Mishra, Shubhanshu (2019): Trained models for multi-task multi-dataset learning for sequence prediction in tweets. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0934773_V1
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
Gallagher, John (2019): NYT vaccine comments. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7724021_V1
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
Yang, Ning; Gao, Jiarong; Lewis, Fred; Yau, Peter; Collins, James; Sweedler, Jonathan; Newmark, Phillip (2019): Data for A novel rotifer derived alkaloid paralyzes schistosome larvae and prevents infection. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1599850_V1
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
de Moya, Robert (2019): Bemisia tabaci ortholog set. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5333299_V1
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
Sashittal, Palash; El-Kebir, Mohammed (2019): SharpTNI Results. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9734610_V1
Results generated using SharpTNI on data collected from the 2014 Ebola outbreak in Sierra Leone.
published: 2019-07-29
Christensen, Sarah; Molloy, Erin K.; Vachaspati, Pranjal; Warnow, Tandy (2019): Data from TRACTION: Fast non-parametric improvement of estimated gene trees. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1747658_V1
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