Dataset Search Results

Data from an a field survey at Nikko National Park in central Japan. Data contain information about deer carcass, environment of sites, and...  vertebrate scavenging.   more description

This dataset contains 1 CSV file: RozanskyLarsonTaylorMsat.csv which contains microsatellite fragment lengths for Virile and Spothanded Cra... yfish from the Current River watershed of Missouri, U.S., and complimentary data, including assignments to species by phenotype and COI sequence data, GenBank accession numbers for COI sequence data, study sites with dates of collection and geographic coordinates, and Illinois Natural History Survey (INHS) Crustacean Collection lots where specimens are stored.   more description

This data set includes survey responses collected during 2015 from academic libraries with library publishing services. Each institution re... sponded to questions related to its use of user studies or information about readers in order to shape digital publication design, formats, and interfaces. Survey data was supplemented with institutional categories to facilitate comparison across institutional types.   more description

# WikiCSSH If you are using WikiCSSH please cite the following: > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020... . “WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.” In Workshop on Scientific Knowledge Graphs (SKG 2020). https://skg.kmi.open.ac.uk/SKG2020/papers/HAN_et_al_SKG_2020.pdf > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. "WikiCSSH - Computer Science Subject Headings from Wikipedia". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0424970_V1 Download the WikiCSSH files from: https://doi.org/10.13012/B2IDB-0424970_V1 More details about the WikiCSSH project can be found at: https://github.com/uiuc-ischool-scanr/WikiCSSH This folder contains the following files: WikiCSSH_categories.csv - Categories in WikiCSSH WikiCSSH_category_links.csv - Links between categories in WikiCSSH Wikicssh_core_categories.csv - Core categories as mentioned in the paper WikiCSSH_category_links_all.csv - Links between categories in WikiCSSH (includes a dummy category called <ROOT> which is parent of isolates and top level categories) WikiCSSH_category2page.csv - Links between Wikipedia pages and Wikipedia Categories in WikiCSSH WikiCSSH_page2redirect.csv - Links between Wikipedia pages and Wikipedia page redirects in WikiCSSH This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.   more description

Prepared by Vetle Torvik 2018-04-15 The dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed... . &bull; How was the dataset created? First and last names of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including Ethnea+Genni as described in: <i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA. http://hdl.handle.net/2142/88927</i> <i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i> EthnicSeer: http://singularity.ist.psu.edu/ethnicity <i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i> SexMachine 0.1.1: <a href="https://pypi.python.org/pypi/SexMachine/">https://pypi.org/project/SexMachine</a> First names, for some Author-ity records lacking them, were harvested from outside bibliographic databases. &bull; The code and back-end data is periodically updated and made available for query at <a href ="http://abel.ischool.illinois.edu">Torvik Research Group</a> &bull; What is the format of the dataset? The dataset contains 9,300,182 rows and 10 columns 1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition) 2. name: full name used as input to EthnicSeer) 3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX 4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction 5. lastname: used as input for Ethnea+Genni 6. firstname: used as input for Ethnea+Genni 7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short) 8. Genni: predicted gender; 'F', 'M', or '-' 9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male) 10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'   more description

This annotation study compared RobotReviewer's data extraction to that of three novice data extractors, using six included articles synthes... ized in one Cochrane review: Bailey E, Worthington HV, van Wijk A, Yates JM, Coulthard P, Afzal Z. Ibuprofen and/or paracetamol (acetaminophen) for pain relief after surgical removal of lower wisdom teeth. Cochrane Database Syst Rev. 2013; CD004624; doi:10.1002/14651858.CD004624.pub2 The goal was to assess the relative advantage of RobotReviewer's data extraction with respect to quality.   more description

We have recently created an approach for high throughput single cell measurements using matrix assisted laser desorption / ionization mass ... spectrometry (MALDI MS) (J Am Soc Mass Spectrom. 2017, 28, 1919-1928. doi: 10.1007/s13361-017-1704-1. Chemphyschem. 2018, 19, 1180-1191. doi: 10.1002/cphc.201701364). While chemical detail is obtained on individual cells, it has not been possible to correlate the chemical information with canonical cell types. Now we combine high-throughput single cell mass spectrometry with immunocytochemistry to determine lipid profiles of two known cell types, astrocytes and neurons from the rodent brain, with the work appearing as “Lipid heterogeneity between astrocytes and neurons revealed with single cell MALDI MS supervised by immunocytochemical classification” (DOI: 10.1002/anie.201812892). Here we provide the data collected for this study. The dataset provides the raw data and script files for the rodent cerebral cells described in the manuscript.   more description

This dataset contains all data used in the two studies included in "PICAN-PI..." by Nute, et al, other than the original raw sequences. Tha... t includes: 1) Supplementary information for the Manuscript, including all the graphics that were created, 2) 16S Reference Alignment, Phylogeny and Taxonomic Annotation used by SEPP, and 3) Data used in the manuscript as input for the graphics generation (namely, SEPP outputs and sequence multiplicities).   more description

This dataset is provided to support the statements in Tarokh, A., and R.Y. Makhnenko. 2019. Remarks on the solid and bulk responses of flui... d-filled porous rock, Geophysics. The unjacketed bulk modulus is a poroelastic parameter that can be directly measured in a laboratory test under a loading that preserves the difference between the mean stress and pore pressure constant. For a monomineralic rock, the measurement of the unjacketed bulk modulus is ignored because it is assumed to be equal to the bulk modulus of the solid phase. To examine this assumption, we tested porous sandstones (Berea and Dunnville) and limestones (Apulian and Indiana) mainly composed of quartz and calcite, respectively, under the unjacketed condition. The presence of microscale inhomogeneities, in the form of non-connected (occluded) pores, was shown to cause a considerable difference between the unjacketed bulk modulus and the bulk modulus of the solid phase. Furthermore, we found the unjacketed bulk modulus to be independent of the unjacketed pressure and Terzaghi effective pressure and therefore a constant.   more description

This dataset contains scripts and data developed as a part of the research manuscript titled “Spatial and Temporal Allocation of Ammonia Em... issions from Fertilizer Application Important for Air Quality Predictions in U.S. Corn Belt”. This includes (1) Spatial and temporal factors for ammonia emissions from agricultural fertilizer usage developed using the hybrid ISS-DNDC method for the Midwest U.S., (2) CAMx job scripts and outputs of predictions of ambient ammonia and total and speciated PM2.5, (3) Observation data used to statistically evaluate CAMx predictions, and (4) MATLAB programs developed to pair CAMx predictions with ground-based observation data in space and time.   more description

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"   more description

Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evide... nce 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   more description

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   more description

Bibliotelemetry data are provided in support of the evaluation of Internet of Things (IoT) middleware within library collections. IoT infra... structure 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.   more description

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>   more description

GBS data from Phaseolus accessions, for a study led by Dr. Glen Hartman, UIUC. <br />The (zipped) fastq file can be processed with th... e 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.   more description

DNDC scripts and outputs that were generated as a part of the research publication 'Evaluation of DeNitrification DeComposition Model for E... stimating Ammonia Fluxes from Chemical Fertilizer Application'.   more description

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.   more description

A 3D CNN method to land cover classification using LiDAR and multitemporal imagery

A dataset of acoustic impulse responses for microphones worn on the body. Microphones were placed at 80 positions on the body of a human su... bject 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.   more description

These show the topography and relief of the Precambrian surface of the Cratonic Platform of the United States.