Dataset Search Results

This repository contains datasets and R scripts that were used in a study of the population structure of Miscanthus sacchariflorus in its native range across East Asia. Notably, genotypes of 764 individuals at 34,605 SNPs, called from reduced-representation DNA sequencing using a non-reference bioinformatics pipeline, are provided. Two similar SNP datasets, used for identifying clonal duplicates and for determining the ancestry of ornamental and hybrid Miscanthus plants identified in previous studies respectively, are also provided. There is also a spreadsheet listing the provenance and ploidy of all individuals along with their plastid (chloroplast) haplotypes. Software output for Structure, Treemix, and DIYABC is also included. See README.txt for more information about individual files. Results of this study are described in a manuscript in revision in Annals of Botany by the same authors, "Population structure of Miscanthus sacchariflorus reveals two major polyploidization events, tetraploid-mediated unidirectional introgression from diploid Miscanthus sinensis, and diversity centered around the Yellow Sea."

GBS data from soybean lines carrying introgressions from Glycine tomentella. This project is led by Dr. Randy Nelson, USDA scientist at the University of Illinois. Fastq files contain raw Illumina data. Txt files are keyfiles containing barcodes for each genetic entity.

GBS data for G. max x G. soja crosses, a project led by Dr. Randy Nelson.

Contains a series of datasets that score pairs of tokens (words, journal names, and controlled vocabulary terms) based on how often they co-occur within versus across authors' collections of papers. The tokens derive from four different fields of PubMed papers: journal, affiliation, title, MeSH (medical subject headings). Thus, there are 10 different datasets, one for each pair of token type: affiliation-word vs affiliation-word, affiliation-word vs journal, affiliation-word vs mesh, affiliation-word vs title-word, mesh vs mesh, mesh vs journal, etc. Using authors to link papers and in turn pairs of tokens is an alternative to the usual within-document co-occurrences, and using e.g., citations to link papers. This is particularly striking for journal pairs because a paper almost always appears in a single journal and so within-document co-occurrences are 0, i.e., useless. The tokens are taken from the Author-ity 2009 dataset which has a cluster of papers for each inferred author, and a summary of each field. For MeSH, title-words, affiliation-words that summary includes only the top-20 most frequent tokens after field-specific stoplisting (e.g., university is stoplisted from affiliation and Humans is stoplisted from MeSH). The score for a pair of tokens A and B is defined as follows. Suppose Ai and Bi are the number of occurrences of token A (and B, respectively) across the i-th author's papers, then nA = sum(Ai); nB = sum(Ai) nAB = sum(Ai*Bi) if A not equal B; nAA = sum(Ai*(Ai-1)/2) otherwise nAnB = nA*nB if A not equal B; nAnA = nA*(nA-1)/2 otherwise score = 1000000*nAB/nAnB if A is not equal B; 1000000*nAA/nAnA otherwise Token pairs are excluded when: score < 5, or nA < cut-off, or nB < cut-off, or nAB < cut-offAB. The cut-offs differ for token types and can be inferred from the datasets. For example, cut-off = 200 and cut-offAB = 20 for journal pairs. Each dataset has the following 7 tab-delimited all-ASCII columns 1: score: roughly the number tokens' co-occurrence divided by the total number of pairs, in parts per million (ppm), ranging from 5 to 1,000,000 2: nAB: total number of co-occurrences 3: nAnB: total number of pairs 4: nA: number of occurrences of token A 5: nB: number of occurrences of token B 6: A: token A 7: B: token B We made some of these datasets as early as 2011 as we were working to link PubMed authors with USPTO inventors, where the vocabulary usage is strikingly different, but also more recently to create links from PubMed authors to their dissertations and NIH/NSF investigators, and to help disambiguate PubMed authors. Going beyond explicit (exact within-field match) is particularly useful when data is sparse (think old papers lacking controlled vocabulary and affiliations, or papers with metadata written in different languages) and when making links across databases with different kinds of fields and vocabulary (think PubMed vs USPTO records). We never published a paper on this but our work inspired the more refined measures described in: <a href="https://doi.org/10.1371/journal.pone.0115681">D′Souza JL, Smalheiser NR (2014) Three Journal Similarity Metrics and Their Application to Biomedical Journals. PLOS ONE 9(12): e115681. https://doi.org/10.1371/journal.pone.0115681</a> <a href="http://dx.doi.org/10.5210/disco.v7i0.6654">Smalheiser, N., & Bonifield, G. (2016). Two Similarity Metrics for Medical Subject Headings (MeSH): An Aid to Biomedical Text Mining and Author Name Disambiguation. DISCO: Journal of Biomedical Discovery and Collaboration, 7. doi:http://dx.doi.org/10.5210/disco.v7i0.6654</a>

Provides links to Author-ity 2009, including records from principal investigators (on NIH and NSF grants), inventors on USPTO patents, and students/advisors on ProQuest dissertations. Note that NIH and NSF differ in the type of fields they record and standards used (e.g., institution names). Typically an NSF grant spanning multiple years is associated with one record, while an NIH grant occurs in multiple records, for each fiscal year, sub-projects/supplements, possibly with different principal investigators. The prior probability of match (i.e., that the author exists in Author-ity 2009) varies dramatically across NIH grants, NSF grants, and USPTO patents. The great majority of NIH principal investigators have one or more papers in PubMed but a minority of NSF principal investigators (except in biology) have papers in PubMed, and even fewer USPTO inventors do. This prior probability has been built into the calculation of match probabilities. The NIH data were downloaded from NIH exporter and the older NIH CRISP files. The dataset has 2,353,387 records, only includes ones with match probability > 0.5, and has the following 12 fields: 1 app_id, 2 nih_full_proj_nbr, 3 nih_subproj_nbr, 4 fiscal_year 5 pi_position 6 nih_pi_names 7 org_name 8 org_city_name 9 org_bodypolitic_code 10 age: number of years since their first paper 11 prob: the match probability to au_id 12 au_id: Author-ity 2009 author ID The NSF dataset has 262,452 records, only includes ones with match probability > 0.5, and the following 10 fields: 1 AwardId 2 fiscal_year 3 pi_position, 4 PrincipalInvestigators, 5 Institution, 6 InstitutionCity, 7 InstitutionState, 8 age: number of years since their first paper 9 prob: the match probability to au_id 10 au_id: Author-ity 2009 author ID There are two files for USPTO because here we linked disambiguated authors in PubMed (from Author-ity 2009) with disambiguated inventors. The USPTO linking dataset has 309,720 records, only includes ones with match probability > 0.5, and the following 3 fields 1 au_id: Author-ity 2009 author ID 2 inv_id: USPTO inventor ID 3 prob: the match probability of au_id vs inv_id The disambiguated inventors file (uiuc_uspto.tsv) has 2,736,306 records, and has the following 7 fields 1 inv_id: USPTO inventor ID 2 is_lower 3 is_upper 4 fullnames 5 patents: patent IDs separated by '|' 6 first_app_yr 7 last_app_yr

This dataset contains all the numerical results (digital elevation models) that are presented in the paper "Landscape evolution models using the stream power incision model show unrealistic behavior when m/n equals 0.5." The paper can be found at: http://www.earth-surf-dynam-discuss.net/esurf-2017-15/ The paper has been accepted, but the most up to date version may not be available at the link above. If so, please contact Jeffrey Kwang at jeffskwang@gmail.com to obtain the most up to date manuscript.

Data used for Zaya et al. (2018), published in Invasive Plant Science and Management DOI 10.1017/inp.2017.37, are made available here. There are three spreadsheet files (CSV) available, as well as a text file that has detailed descriptions for each file ("readme.txt"). One spreadsheet file ("prices.csv") gives pricing information, associated with Figure 3 in Zaya et al. (2018). The other two spreadsheet files are associated with the genetic analysis, where one file contains raw data for biallelic microsatellite loci ("genotypes.csv") and the other ("structureResults.csv") contains the results of Bayesian clustering analysis with the program STRUCTURE. The genetic data may be especially useful for future researchers. The genetic data contain the genotypes of the horticultural samples that were the focus of the published article, and also genotypes of nearly 400 wild plants. More information on the location of the wild plant collections can be found in the Supplemental information for Zaya et al. (2015) Biological Invasions 17:2975–2988 DOI 10.1007/s10530-015-0926-z. See "readme.txt" for more information.

These are the results of an 8 month cohort study in two commercial dairy herds in Northwest Illinois. From each herd, 50 cows were selected at random, stratified over lactations 1 to 3. Serum from these animals was collected every two months and tested for antibodies to Bovine Leukosis Virus, Neospora caninum, and Mycobacterium avium subsp. paratuberculosis. Animals that left the herd during the study were replaced by another animal in the same herd and lactation. At the last sampling, serum neutralization assays were performed for Bovine Herpesvirus type 1 and Bovine Viral Diarrhea virus type 1 and 2. Production data before and after sampling was collected for the entire herd from PCdart.

This dataset matches to a thesis of the same title: Can fair use be adequately taught to Librarians? Assessing Librarians' confidence and comprehension in explaining fair use following an expert workshop.

Objectives: This study follows-up on previous work that began examining data deposited in an institutional repository. The work here extends the earlier study by answering the following lines of research questions: (1) what is the file composition of datasets ingested into the University of Illinois at Urbana-Champaign campus repository? Are datasets more likely to be single file or multiple file items? (2) what is the usage data associated with these datasets? Which items are most popular? Methods: The dataset records collected in this study were identified by filtering item types categorized as "data" or "dataset" using the advanced search function in IDEALS. Returned search results were collected in an Excel spreadsheet to include data such as the Handle identifier, date ingested, file formats, composition code, and the download count from the item's statistics report. The Handle identifier represents the dataset record's persistent identifier. Composition represents codes that categorize items as single or multiple file deposits. Date available represents the date the dataset record was published in the campus repository. Download statistics were collected via a website link for each dataset record and indicates the number of times the dataset record has been downloaded. Once the data was collected, it was used to evaluate datasets deposited into IDEALS. Results: A total of 522 datasets were identified for analysis covering the period between January 2007 and August 2016. This study revealed two influxes occurring during the period of 2008-2009 and in 2014. During the first time frame a large number of PDFs were deposited by the Illinois Department of Agriculture. Whereas, Microsoft Excel files were deposited in 2014 by the Rare Books and Manuscript Library. Single file datasets clearly dominate the deposits in the campus repository. The total download count for all datasets was 139,663 and the average downloads per month per file across all datasets averaged 3.2. Conclusion: Academic librarians, repository managers, and research data services staff can use the results presented here to anticipate the nature of research data that may be deposited within institutional repositories. With increased awareness, content recruitment, and improvements, IRs can provide a viable cyberinfrastructure for researchers to deposit data, but much can be learned from the data already deposited. Awareness of trends can help librarians facilitate discussions with researchers about research data deposits as well as better tailor their services to address short-term and long-term research needs.

The dataset contains processed model fields used to generate data, figures and tables in the Journal of Geophysical Research article "Investigating the linear dependence of direct and indirect radiative forcing on emission of carbonaceous aerosols in a global climate model." The processed data are monthly averaged cloud properties (CCN, CDNC and LWP) and forcing variables (DRF and IRF) at original CAM5 spatial resolution (1.9° by 2.5°). Raw model output fields from CAM5 simulations are available through NERSC upon request. Please find more detailed information in the ReadMe file.

This dataset provides the time series (Aug. - Sep. 2016) data of sun-induced chlorophyll fluorescence, photosynthesis, photosynthetically active radiation, and associated vegetation indices that were collected in a soybean field in the farm of University of Illinois at Urbana and Champaign. Data contain 255 records and 6 variables (PPFD-IN: Photosynthetically active radiation; GPP-Gross Primary Production; SIF: Sun-Induced Fluorescence; NDVI: Normalized Difference Vegetation Index; Rededge: Rededge Index; Redege_NDVI: Rededge Normalized Difference Vegetation Index). The timestamp uses the standard time. Data are available from 8 am to 4 pm (corresponding to 9 am to 5 pm local time) every day.

Datasets used in the study, "OCTAL: Optimal Completion of Gene Trees in Polynomial Time," under review at Algorithms for Molecular Biology. Note: DS_STORE file in 25gen-10M folder can be disregarded.

Table S1. Pollen types identified in the BCI and PNSL pollen rain data sets. Pollen types were identified to species when possible and assigned a life form based on descriptions provided in Croat, T.B. (1978). Taxa from BCI and PNSL were assigned a 1 if present in forest census data or a 0 if absent. The relative representation of each taxon has been provided for each extended record and by dry and wet season representation respectively. CA loadings are provided for axes 1 and 2 (Fig. 1).

This dataset contains hourly traffic estimates (speeds) for individual links of the New York City road network for the years 2010-2013, estimated from New York City Taxis.

Monthly water withdrawal records (total pumpage and per-capita consumption) for the City of Austin, Texas (2000-2014). Data were provided by Austin Water Utility.

This dataset contains ground motion data for Newmark Structural Engineering Laboratory (NSEL) Report Series 048, "Modification of ground motions for use in Central North America: Southern Illinois surface ground motions for structural analysis". The data are 20 individual ground motion time history records developed at each of the 10 sites (for a total of 200 ground motions). These accompanying ground motions are developed following the detailed procedure presented in Kozak et al. [2017].

This is the dataset used in the Journal of Ecology publication of the same name. It is a site by species matrix of species relative abundances. The file BH.veg.data.csv contains a site by species matrix of species relative abundance (percent cover across all sampling quadrats within site). Data under the heading Year refers to sampling periods. Year 1 refers to the first set of samples taken between 1997 and 2000, Year 2 refers to the second set taken between 2002 and 2005, Year 3 refers to the third set taken between 2007 and 2010, and Year 4 refers to the fourth set taken between 2012 and 2015. All sites met Critical Trends Assessment Program (CTAP) size criteria of being at least 2 ha in size with a minimum of 500 m2 of suitable sampling area. The data in file BH.site.location.csv contains Public Land Survey System ranges and townships in which specific sites were located. All sites were located within the U.S. state of Illinois. More information about this dataset: Interested parties can request data from the Critical Trends Assessment Program, which was the source for the data on the wetlands in this study. More information on the program and data requests can be obtained by visiting the program webpage. Critical Trends Assessment Program, Illinois Natural History Survey. http://wwx.inhs.illinois.edu/research/ctap/

This file contains the supplemental appendix for the article "Farmer Preferences for Agricultural Soil Carbon Sequestration Schemes" published in Applied Economic Policy and Perspectives (accepted 2017).

Spire angle data for sinistral whelks of the family Busyconidae. Data focuses on spire angles, with some data on total shell length. Locality information is present for all modern specimens.

This dataset contains the PartMC-MOSAIC simulations used in the article “Plume-exit modeling to determine cloud condensation nuclei activity of aerosols from residential biofuel combustion”. The data is organized as a set of folders, each folder representing a different scenario modeled. Each folder contains a series of NetCDF files, which are the output of the PartMC-MOSAIC simulation. They contain information on particle and gas properties, both of the biofuel burning plume and background. Input files for PartMC-MOSAIC are also included. This dataset was used during the open review process at Atmospheric Chemistry and Physics (ACP) and supports both the discussion paper and final article.

Table S2. Raw pollen counts and climatic data for each seasonal sampling period. Climatic data reflects the average daily conditions observed over the duration samples were collected (˚C/day, mm/day, MJ/m2/day). Lycopodium counts and counts for each pollen taxon reflect the aggregated pollen sum from four sampling heights.

Table S3. Mean slope response for each predictive model used in the ecoinformatic analysis. Mean responses are provided for each seasonal and annual pollen data set analyzed from BCI and PNSL and are summarized by life form. Calculated p-values are provided for each model.

Datasets used in the study, "Optimal completion of incomplete gene trees in polynomial time using OCTAL," presented at WABI 2017.

Dataset includes maternal antigen treatment and early-life antigen treatment for male zebra finches. Also includes data on beak coloration, measures of song complexity for each male, and female responses to treated males. Male beak color and song metadata: * MATID= Maternal Identity * MATTRT=Maternal antigen treatment prior to egg laying (KLH=keyhole limpet hemocyanin, LPS= lipopolysaccharide, PBS=phosphate buffered saline) * YGTRT= Young antigen treatment post-hatch (KLH=keyhole limpet hemocyanin, LPS= lipopolysaccharide, PBS=phosphate buffered saline)) * NESTBANDNUM= Nestling band number * Haptoglobin=haptoglobin levels at day 28 (mg/ml) * Mean TE= Mean number of total elements in that male's song * TE (z)= Z-transformed total elements * Mean UE=Mean number of unique elements in the song * UE (z)= z-transformed unique elements * mean phrases= Mean number of song phrases * Phrases (z)= z-transformed song phrases * Mean D= Mean song duration in seconds * D (z)=z-transformed song duration * B2 standard=beak brightness standardized so that lower values reflect less bright beaks * B2 (z)=z-transformed brightness * S1R standard= beak saturation at high wavelengths standardized so that lower values reflect less red beaks * S1R (z)=z-transformed S1R * S1U standard= beak saturation at low wavelengths standardized so that lower values reflect less red beaks * S1U (z)=z-transformed S1U * H4B standard= beak hue standardized so that lower values reflect less red beaks * H4B (z)=z-transformed H4B Female choice metadata: * Control Bird=PBS denotes that all control males received phosphate buffered saline * Treatment Bird= Treatment the male received (keyhole limpet hemocyanin (KLH) or lipopolysaccharide (LPS)) * Beak Wipes Control=# of beak wipes the female performed when on the control male side * Beak Wipes Treatment=# of beak wipes the female performed when on the "treatment male" side * Hops Control=# of hops female performed when on the control male side * Hops Treatment=# of hops female performed when on the treatment male side * Time Spent Near Control=amount of time (sec) female spent on the control male side * Time Spent Near Treatment=amount of time (sec) the female spent on the treatment male side