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Illinois Data Bank Dataset Search Results
Dataset Search Results
published: 2019-05-31
Krichels, Alexander (2019): Data for Dynamic controls on field-scale soil nitrous oxide hot spots and hot moments across a microtopographic gradient. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9733959_V1
This dataset includes all data presented in the manuscript entitled: "Dynamic controls on field-scale soil nitrous oxide hot spots and hot moments across a microtopographic gradient"
keywords:
denitrification; depressions; microtopography; nitrous oxide; soil oxygen; soil temperature
published: 2019-06-22
MacDonald, Sean; Ward, Michael; Sperry, Jinelle (2019): Manipulating social information to promote frugivory by birds on a Hawaiian Island. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9223847_V1
keywords:
conspecific attraction; fruit-eating bird; Hawaiian flora; playback experiment; seed dispersal; social information; Zosterops japonicas
published: 2018-01-11
Pence, Justin; Mohaghegh, Zahra (2018): DT-BASE - Training Quality Causal Model. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3357538_V3
Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level. (M1, M2, and M3) Weight metadata are based on probability language from <a href="https://www.ipcc.ch/ipccreports/tar/vol4/english/index.htm" style="text-decoration: none" >Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report</a>. The language can be found in the “Summary for Policymakers” section, in the PDF format. Weight Metadata: LowerBound_Probability, UpperBound_Probability, Qualitative Language 0.99, 1, Virtually Certain 0.9, 0.99, Very Likely 0.66, 0.9, Likely 0.33, 0.66, Medium Likelihood 0.1, 0.33, Unlikely 0.01, 0.1, Very Unlikely 0, 0.01, Extremely Unlikely
keywords:
Data-Theoretic; Training; Organization; Probabilistic Risk Assessment; Training Quality; Causal Model; DT-BASE; Bayesian Belief Network; Bayesian Network; Theory-Building
published: 2018-01-03
Sweet, Andrew; Bush, Sarah; Gustafsson, Daniel; Allen, Julie; DiBlasi, Emily; Skeen, Heather; Weckstein, Jason; Johnson, Kevin (2018): Data from: host and parasite morphology influence congruence between host and parasite phylogenies. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2011663_V1
Concatenated sequence alignment, phylogenetic analysis files, and relevant software parameter files from a cophylogenetic study of Brueelia-complex lice and their avian hosts. The sequence alignment file includes a list of character blocks for each gene alignment and the parameters used for the MrBayes phylogenetic analysis. 1) Files from the MrBayes analyses: a) a file with 100 random post-burnin trees (50% burnin) used in the cophylogenetic analysis - analysisrandom100_trees_brueelia.tre b) a majority rule consensus tree - treeconsensus_tree_brueelia.tre c) a maximum clade credibility tree - mcc_tree_brueelia.tre The tree tips are labeled with louse voucher names, and can be referenced in Supplementary Table 1 of the associated publication. 2) Files related to a BEAST analysis with COI data: a) the XML file used as input for the BEAST run, including model parameters, MCMC chain length, and priors - beast_parameters_coi_brueelia.xml b) a file with 100 random post-burnin trees (10% burnin) from the BEAST posterior distribution of trees; used in OTU analysis - beast_100random_trees_brueelia.tre c) an ultrametric maximum clade credibility tree - mcc_tree_beast_brueelia.tre 3) A maximum clade credibility tree of Brueelia-complex host species generated from a distribution of trees downloaded from https://birdtree.org/subsets/ - mcc_tree_brueelia_hosts.tre 4) Concatenated sequence alignment - concatenated_alignment_brueelia.nex
keywords:
bird lice; Brueelia-complex; passerines; multiple sequence alignment; phylogenetic tree; Bayesian phylogenetic analysis; MrBayes; BEAST
published: 2018-03-08
Imker, Heidi (2018): Molecular Biology Databases Published in Nucleic Acids Research between 1991-2016. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4311325_V1
This dataset was developed to create a census of sufficiently documented molecular biology databases to answer several preliminary research questions. Articles published in the annual Nucleic Acids Research (NAR) “Database Issues” were used to identify a population of databases for study. Namely, the questions addressed herein include: 1) what is the historical rate of database proliferation versus rate of database attrition?, 2) to what extent do citations indicate persistence?, and 3) are databases under active maintenance and does evidence of maintenance likewise correlate to citation? An overarching goal of this study is to provide the ability to identify subsets of databases for further analysis, both as presented within this study and through subsequent use of this openly released dataset.
keywords:
databases; research infrastructure; sustainability; data sharing; molecular biology; bioinformatics; bibliometrics
published: 2018-03-28
Hahn, James (2018): Bibliotelemetry data. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2237685_V2
Bibliotelemetry data are provided in support of the evaluation of Internet of Things (IoT) middleware within library collections. IoT infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. A modular evaluation and analysis herein clarified the nature of users’ requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of bibliographic identifiers as they appeared throughout the global module system, serving to provide context to the searching and browsing data that are the focus of this study.
keywords:
internet of things; IoT; academic libraries; bibliographic classification
published: 2018-04-06
Collins, Kodi; Warnow, Tandy (2018): PASTA For Proteins Data (BALiBASE). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4074787_V1
keywords:
protein; multiple sequence alignment; balibase
published: 2018-04-23
Mishra, Shubhanshu; Torvik, Vetle I. (2018): Conceptual novelty scores for PubMed articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5060298_V1
Conceptual novelty analysis data based on PubMed Medical Subject Headings ---------------------------------------------------------------------- Created by Shubhanshu Mishra, and Vetle I. Torvik on April 16th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Torvik VI. Quantifying Conceptual Novelty in the Biomedical Literature. D-Lib magazine : the magazine of the Digital Library Forum. 2016;22(9-10):10.1045/september2016-mishra. It contains final data generated as part of our experiments based on MEDLINE 2015 baseline and MeSH tree from 2015. The dataset is distributed in the form of the following tab separated text files: * PubMed2015_NoveltyData.tsv - Novelty scores for each paper in PubMed. The file contains 22,349,417 rows and 6 columns, as follow: - PMID: PubMed ID - Year: year of publication - TimeNovelty: time novelty score of the paper based on individual concepts (see paper) - VolumeNovelty: volume novelty score of the paper based on individual concepts (see paper) - PairTimeNovelty: time novelty score of the paper based on pair of concepts (see paper) - PairVolumeNovelty: volume novelty score of the paper based on pair of concepts (see paper) * mesh_scores.tsv - Temporal profiles for each MeSH term for all years. The file contains 1,102,831 rows and 5 columns, as follow: - MeshTerm: Name of the MeSH term - Year: year - AbsVal: Total publications with that MeSH term in the given year - TimeNovelty: age (in years since first publication) of MeSH term in the given year - VolumeNovelty: : age (in number of papers since first publication) of MeSH term in the given year * meshpair_scores.txt.gz (36 GB uncompressed) - Temporal profiles for each MeSH term for all years - Mesh1: Name of the first MeSH term (alphabetically sorted) - Mesh2: Name of the second MeSH term (alphabetically sorted) - Year: year - AbsVal: Total publications with that MeSH pair in the given year - TimeNovelty: age (in years since first publication) of MeSH pair in the given year - VolumeNovelty: : age (in number of papers since first publication) of MeSH pair in the given year * README.txt file ## Dataset creation This dataset was constructed using multiple datasets described in the following locations: * MEDLINE 2015 baseline: <a href="https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html">https://www.nlm.nih.gov/bsd/licensee/2015_stats/baseline_doc.html</a> * MeSH tree 2015: <a href="ftp://nlmpubs.nlm.nih.gov/online/mesh/2015/meshtrees/">ftp://nlmpubs.nlm.nih.gov/online/mesh/2015/meshtrees/</a> * Source code provided at: <a href="https://github.com/napsternxg/Novelty">https://github.com/napsternxg/Novelty</a> Note: The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in the first week of October, 2016. Check <a href="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">here </a>for information to get PubMed/MEDLINE, and NLMs data Terms and Conditions: Additional data related updates can be found at: <a href="http://abel.ischool.illinois.edu">Torvik Research Group</a> ## Acknowledgments This work was made possible in part with funding to VIT from <a href="https://projectreporter.nih.gov/project_info_description.cfm?aid=8475017&icde=18058490">NIH grant P01AG039347 </a> and <a href="http://www.nsf.gov/awardsearch/showAward?AWD_ID=1348742">NSF grant 1348742 </a>. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## License Conceptual novelty analysis data based on PubMed Medical Subject Headings by Shubhanshu Mishra, and Vetle Torvik is licensed under a Creative Commons Attribution 4.0 International License. Permissions beyond the scope of this license may be available at <a href="https://github.com/napsternxg/Novelty">https://github.com/napsternxg/Novelty</a>
keywords:
Conceptual novelty; bibliometrics; PubMed; MEDLINE; MeSH; Medical Subject Headings; Analysis;
published: 2018-05-21
Karigerasi, Manohar H.; Wagner, Lucas K.; Shoemaker, Daniel P. (2018): Geometric analysis of magnetic dimensionality. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3897093_V1
This dataset contains bonding networks and tolerance ranges for geometric magnetic dimensionality. The data can be searched in the html frontend above, code obtained at the GitHub repository, or the raw data can be downloaded as csv below. The csv data contains the results of 42520 compounds (unique icsd_code) from ICSD FindIt v3.5.0. The csv is semicolon-delimited since some fields contain multiple comma-separated values.
keywords:
materials science; physics; magnetism; crystallography
published: 2018-04-05
Brown, Patrick (2018): Phaseolus GBS. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6255617_V1
GBS data from Phaseolus accessions, for a study led by Dr. Glen Hartman, UIUC. <br />The (zipped) fastq file can be processed with the TASSEL GBS pipeline or other pipelines for SNP calling. The related article has been submitted and the methods section describes the data processing in detail.
published: 2018-06-06
Balasubramanian, Srinidhi; Nelson, Andrew; Koloutsou-Vakakis, Sotiria; Lin, Jie; Rood, Mark; Myles, LaToya; Bernacchi, Carl (2018): Dataset for Evaluation of DeNitrification DeComposition Model for Estimating Ammonia Fluxes from Chemical Fertilizer Application. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-3773381_V1
DNDC scripts and outputs that were generated as a part of the research publication 'Evaluation of DeNitrification DeComposition Model for Estimating Ammonia Fluxes from Chemical Fertilizer Application'.
keywords:
DNDC; REA; ammonia emissions; fertilizers; uncertainty analysis
published: 2018-07-25
Scannapieco, Frank; Hoang, Linh; Schneider, Jodi (2018): Expert assessment of RobotReviewer data extraction performance on 10 articles. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8274875_V1
The PDF describes the process and data used for the heuristic user evaluation described in the related article “<i>Evaluating an automatic data extraction tool based on the theory of diffusion of innovation</i>” by Linh Hoang, Frank Scannapieco, Linh Cao, Yingjun Guan, Yi-Yun Cheng, and Jodi Schneider (under submission).<br /> Frank Scannapieco assessed RobotReviewer data extraction performance on ten articles in 2018-02. Articles are included papers from an update review: Sabharwal A., G.-F.I., Stellrecht E., Scannapeico F.A. <i>Periodontal therapy to prevent the initiation and/or progression of common complex systemic diseases and conditions</i>. An update. Periodontol 2000. In Press. <br/> The form was created in consultation with Linh Hoang and Jodi Schneider. To do the assessment, Frank Scannapieco entered PDFs for these ten articles into RobotReviewer and then filled in ten evaluation forms, based on the ten Robot Reviewer automatic data extraction reports. Linh Hoang analyzed these ten evaluation forms and synthesized Frank Scannapieco’s comments to arrive at the evaluation results for the heuristic user evaluation.
keywords:
RobotReviewer; systematic review automation; data extraction
published: 2018-08-01
Clark, Lindsay V.; Lipka, Alexander E.; Sacks, Erik J. (2018): Scripts for testing the error rate of polyRAD. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9729830_V1
This set of scripts accompanies the manuscript describing the R package polyRAD, which uses DNA sequence read depth to estimate allele dosage in diploids and polyploids. Using several high-confidence SNP datasets from various species, allelic read depth from a typical RAD-seq dataset was simulated, then genotypes were estimated with polyRAD and other software and compared to the true genotypes, yielding error estimates.
keywords:
R programming language; genotyping-by-sequencing (GBS); restriction site-associated DNA sequencing (RAD-seq); polyploidy; single nucleotide polymorphism (SNP); Bayesian genotype calling; simulation
published: 2018-08-03
Kim, Eun Sun; Zaya, David N.; Fant, Jeremie B.; Ashley, Mary V. (2018): Castilleja coccinea fruit set and seed set data from Illinois Beach State Park . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0852856_V2
These data include information on a field experiment on Castilleja coccinea (L.) Spreng., scarlet Indian paintbrush (Orobanchaceae). There is intraspecific variation in scarlet Indian paintbrush in the color of the bracts surrounding the flowers. Two bract color morphs were included in this study, the scarlet and yellow morphs. The experiment was conducted at Illinois Beach State Park in 2012. The aim of the work was to compare the color morphs with regard to 1) self-compatibility, 2) response to pollinator exclusion, 3) cross-compatibility between the color morphs, and 4) relative female fertility and male fitness. Three files are attached with this record. The raw data are in "fruitSet.csv" and "seedSet.csv", while "readme.txt" has detailed explanations of the raw data files.
keywords:
Castilleja coccinea; Orobanchaceae; floral color polymorphism; bract color polymorphism; breeding system; hand-pollination; self-compatibility; reproductive assurance
published: 2018-11-21
Clark, Lindsay V.; Lipka, Alexander E.; Sacks, Erik J. (2018): Scripts for testing the error rate of polyRAD. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9729830_V2
This set of scripts accompanies the manuscript describing the R package polyRAD, which uses DNA sequence read depth to estimate allele dosage in diploids and polyploids. Using several high-confidence SNP datasets from various species, allelic read depth from a typical RAD-seq dataset was simulated, then genotypes were estimated with polyRAD and other software and compared to the true genotypes, yielding error estimates.
keywords:
R programming language; genotyping-by-sequencing (GBS); restriction site-associated DNA sequencing (RAD-seq); polyploidy; single nucleotide polymorphism (SNP); Bayesian genotype calling; simulation
published: 2018-12-13
Xu, Zewei; Wang, Shaowen (2018): A 3DCNN-based method to land cover classification. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0024113_V1
A 3D CNN method to land cover classification using LiDAR and multitemporal imagery
keywords:
3DCNN; land cover classification; LiDAR; multitemporal imagery
published: 2018-11-20
Corey, Ryan M.; Tsuda, Naoki; Singer, Andrew C. (2018): Wearable Microphone Impulse Responses. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1932389_V1
A dataset of acoustic impulse responses for microphones worn on the body. Microphones were placed at 80 positions on the body of a human subject and a plastic mannequin. The impulse responses can be used to study the acoustic effects of the body and can be convolved with sound sources to simulate wearable audio devices and microphone arrays. The dataset also includes measurements with different articles of clothing covering some of the microphones and with microphones placed on different hats and accessories. The measurements were performed from 24 angles of arrival in an acoustically treated laboratory. Related Paper: Ryan M. Corey, Naoki Tsuda, and Andrew C. Singer. "Acoustic Impulse Responses for Wearable Audio Devices," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, May 2019. All impulse responses are sampled at 48 kHz and truncated to 500 ms. The impulse response data is provided in WAVE audio and MATLAB data file formats. The microphone locations are provided in tab-separated-value files for each experiment and are also depicted graphically in the documentation. The file wearable_mic_dataset_full.zip contains both WAVE- and MATLAB-format impulse responses. The file wearable_mic_dataset_matlab.zip contains only MATLAB-format impulse responses. The file wearable_mic_dataset_wave.zip contains only WAVE-format impulse responses.
keywords:
Acoustic impulse responses; microphone arrays; wearables; hearing aids; audio source separation
published: 2016-11-28
Marshak, Stephen; Domrois, Stefanie; Abert, Curtis; Larson, Timothy (2016): DEM of the Great Unconformity, USA cratonic platform. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7546972_V1
These show the topography and relief of the Precambrian surface of the Cratonic Platform of the United States.
keywords:
precambrian; geology; relief; elevation
published: 2016-12-12
Zhang, Qian; Li, Chunyan (2016): Bathymetry data of the Wax Lake delta (late 2012). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1001307_V1
This dataset is the field measurements of water depth at the Wax Lake delta conducted in late 2012.
keywords:
Wax Lake delta; Bathymetry
published: 2016-12-12
Zhang, Qian; Li, Chunyan (2016): Current data of the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-1752285_V1
This dataset is the field measurements of currents at two stations (Big Hogs Bayou and Delta1) in the the Wax Lake delta in November 2012 and February 2013.
keywords:
Wax Lake delta; Currents
published: 2016-12-12
Zhang, Qian; Li, Chunyan (2016): Bathymetry data of the Wax Lake delta (2012-12-01). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4810873_V1
This dataset is the field measurements of water depth at the Wax Lake delta on the date 2012-12-01.
keywords:
Wax Lake delta; Bathymetry
published: 2016-12-12
Zhang, Qian (2016): Public agency data of the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4871125_V1
This dataset includes data of the the Wax Lake delta from four public agencies: NGDC, USGS, NDBC, and NOAA CO-OPS. Besides the original data, the processed data associated with analyzed figures are also shared.
keywords:
Wax Lake delta; NOAA CO-OPS; NGDC; USGS; NDBC
published: 2016-12-18
Zhang, Qian; Li, Chunyan (2016): Model dataset for the Wax Lake delta. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9511904_V1
This dataset is the numerical simulation data of the computational study of the cold front-related hydrodynamics in the Wax Lake delta. The numerical model used is ECOM-si.
keywords:
Wax Lake delta; Hydrodynamics; Cold front
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: 2019-10-15
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