Dataset Search Results

File Name: WordsSelectedByInformationGain.csv Data Preparation: Xiaoru Dong, Linh Hoang Date of Preparation: 2018-12-12 Data Contributions: Jingyi Xie, Xiaoru Dong, Linh Hoang Data Source: Cochrane systematic reviews published up to January 3, 2018 by 52 different Cochrane groups in 8 Cochrane group networks. Associated Manuscript authors: Xiaoru Dong, Jingyi Xie, Linh Hoang, and Jodi Schneider. Associated Manuscript, Working title: Machine classification of inclusion criteria from Cochrane systematic reviews. Description: the file contains a list of 1655 informative words selected by applying information gain feature selection strategy. Information gain is one of the methods commonly used for feature selection, which tells us how many bits of information the presence of the word are helpful for us to predict the classes, and can be computed in a specific formula [Jurafsky D, Martin JH. Speech and language processing. London: Pearson; 2014 Dec 30].We ran Information Gain feature selection on Weka -- a machine learning tool. Notes: In order to reproduce the data in this file, please get the code of the project published on GitHub at: https://github.com/XiaoruDong/InclusionCriteria and run the code following the instruction provided.

Ammonia flux measurement data using flux gradient and relaxed eddy accumulation methods, and ancillary environmental data collected during the 2014 corn-growing season in Central Illinois, USA. This excel file contains two spreadsheets: one README sheet, and one sheet containing all data. These data were used in the development of the manuscript titled "Ammonia Flux Measurements above a Corn Canopy using Relaxed Eddy Accumulation and a Flux Gradient System."

This is the dataset used in the Ecological Applications publication of the same name. This dataset consists of the following files: Internal.Community.Data.txt Regional.Community.Data.txt Site.Attributes.txt Year.Of.Final.Bio.Monitoring.txt Internal.Community.Data.txt is a site and plot by species matrix. Column labeled SITE consists of site IDs. Column labeled Plot consists of Plot numbers. All other columns represent species relative abundances per plot. Regional.Community.Data.txt is a site by species matrix of relative abundances. Column labeled site consists of site IDs. All other columns represent species relative abundances per site. Site.attributes.txt is a matrix of site attributes. Column labeled SITE consists of site IDs. Column labeled Long represents longitude in decimal degrees. Column labeled Lat represents latitude in decimal degrees. Column labeled Richness represents species richness of sites calculated from Regional Community Data. Column labeled NAT_COMP_REST represents designation as a randomly selected natural wetland (NAT), compensation wetland (COMP) or reference quality natural wetland (REF). Column labeled HQ_LQ_COMP represents designation as high quality (HQ), low quality (LQ) or compensation wetland (COMP). Column labeled SAMPLING_YEAR_INTERNAL represents year data used for analysis of internal β-diversity was gathered. Column labeled SAMPLING_YEAR_REGIONAL represents year data used for analysis of regional β-diversity was gathered. Column labeled TRANSECT_LENGTH represents length in meters of initial sampling transect. INAI_GRADE represents Illinois Natural Areas Inventory grades assigned to each site. Grades range from A for highest quality natural areas to E for lowest quality natural areas. Year.Of.Final.Bio.Monitoring.txt is a table representing years of final monitoring of compensation wetlands as mandated by the US Army Corps of Engineers. Column labeled Site consists of site IDs. Column labeled YR_FIN_BIO_MON consists of years of final monitoring. Entries of N/A represent dates that were unable to be located. More information about this dataset: Interested parties can request data from the Critical Trends Assessment Program, which was the source for data on naturally occurring 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/

Supplementary Material for article entitled: "Identifying marginal land for multifunctional perennial cropping systems in the Upper Sangamon River Watershed, Illinois". The material includes the methodology of GIS RUSLE model and details of the suitability analysis variables.

This dataset contains records of five years of interlibrary loan (ILL) transactions for the University of Illinois at Urbana-Champaign Library. It is for the materials lent to other institutions during period 2009-2013. It includes 169,890 transactions showing date; borrowing institution’s type, state and country; material format, imprint city, imprint country, imprint region, call number, language, local circulation count, ILL lending count, and OCLC holdings count. The dataset was generated putting together monthly ILL reports. Circulation and ILL lending fields were added from the ILS records. Borrower region and imprint region fields are created based on Title VI Region List. OCLC holdings field has been added from WorldCat records.

Weather data used in the survival (mark-recapture) analysis of Swainson's Thrushes crossing the Gulf of Mexico

Data used to estimate the survival of Swainson's Thrushes crossing the Gulf of Mexico.

These data are for two companion papers on use of LSPIV obtained from UAS (i.e. drones) to measure flow structure in streams. The LSPIV1 folder contains spreadsheet data used in each case referred to in Table 1 in the manuscript. In the spreadsheets, there is a cell that denotes which figure was constructed with which data. The LSPIV2 folder contains spreadsheets with data used for the constructed figures, and are labeled by figure.

This dataset presents a citation analysis and citation context analysis used in Linh Hoang, Frank Scannapieco, Linh Cao, Yingjun Guan, Yi-Yun Cheng, and Jodi Schneider. Evaluating an automatic data extraction tool based on the theory of diffusion of innovation. Under submission. We identified the papers that directly describe or evaluate RobotReviewer from the list of publications on the RobotReviewer website <http://www.robotreviewer.net/publications>, resulting in 6 papers grouped into 5 studies (we collapsed a conference and journal paper with the same title and authors into one study). We found 59 citing papers, combining results from Google Scholar on June 05, 2018 and from Scopus on June 23, 2018. We extracted the citation context around each citation to the RobotReviewer papers and categorized these quotes into emergent themes.

Qualitative Data collected from the websites of undergraduate research journals between October, 2014 and May, 2015. Two CSV files. The first file, "Sample", includes the sample of journals with secondary data collected. The second file, "Population", includes the remainder of the population for which secondary data was not collected. Note: That does not add up to 800 as indicated in article, rows were deleted for journals that had broken links or defunct websites during random sampling process.

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.