Dataset Search

Landscape attributes of the nineteen sites as supplemental data for the following article: Bennett, A.B., Lovell, S.T. 2019. Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. Accepted for publication in: PLOS ONE.

These are the responses to an open, convenience sample survey of residents of Illinois to understand their interactions with wild deer. The survey was available on REDCap between December 19, 2022 and December 19, 2023, and was publicized through listserves, Facebook groups, and media reporting. The file "COVID Deer Survey _ REDCap.pdf" contains the codebook for the survey, including the questions; all factor variables have ".factor" added to their name in the dataset. The file "DeerSurveyData.csv" contains the dataset. The file "Score_calculation_for_sharing.R" is the code to create the cleaned dataset used for analysis from the raw survey responses. Throughout, NA is used to represent null/not available/not applicable; this is most likely either a failure to answer the question or, in some cases, a question that was not presented as it is not relevant based on answers to previous questions.

These data were collected in 2018 and 2019 at the University of Illinois Energy Farm (N 40.063607, W 88.206926). During each growing season, bulk and rhizosphere soil were collected from replicate Sorghum bicolor nitrogen use efficiency trial plots at three separate time points (approximately July 1, August 1, and September 1). We measured soil moisture, pH, soil nitrate and ammonium, potential nitrification, potential denitrification, and extracted and sequenced the V4 region of the 16S rRNA gene for microbial community analysis. All microbial sequence data is archived in the National Center for Biotechnology Information’s (NCBI) Sequence Read Archive (accession number SRP326979, project number PRJNA741261).

The following files were used to reconstruct the phylogeny of the leafhopper subfamily Typhlocybinae, using IQ-TREE v1.6.12 and ASTRAL v 4.10.5. <b>1) Taxon_sampling.csv:</b> contains the sample IDs (1st column) and the taxonomic information (2nd column). Sample IDs were used in the alignment files and partition files. <b>2) concatenated_nt_complete.phy:</b> a complete concatenated nucleotide dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 154,992 nucleotide positions (intron included) from 665 loci. Hyphens are used to represent gaps. <b>3) concatenated_nt_complete_partition.nex:</b> the partitioning schemes for concatenated_nt_complete.phy. The file partitions the 154,992 nucleotide characters into 426 character sets, and defines the best substitution model for each character set. <b>4) concatenated_cds_complete.phy:</b> a complete concatenated coding DNA sequence dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 153,525 nucleotide positions (intron excluded) from 665 loci. Hyphens are used to represent gaps. <b>5) concatenated_cds_complete_partition.nex:</b> the partitioning schemes for concatenated_cds_complete.phy. The file partitions the 153,525 nucleotide characters into 426 character sets, and defines the best substitution model for each character set. <b>6) concatenated_nt_reduced.phy:</b> a reduced concatenated nucleotide dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12. The file lists the sequences of 248 samples with 95,076 nucleotide positions (intron included) from 374 loci. Hyphens are used to represent gaps. <b>7) concatenated_nt_reduced_partition.nex:</b> the partitioning schemes for concatenated_nt_reduced.phy. The file partitions the 95,076 nucleotide characters into 312 character sets, and defines the best substitution model for each character set. <b>8) concatenated_aa_complete.phy:</b> a complete concatenated amino acid dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12, corresponding to concatenated_cds_complete.phy. The file lists the sequences of 248 samples with 51,175 amino acid positions from 665 loci. Hyphens are used to represent gaps. <b>9) concatenated_aa_complete_partition.nex:</b> the partitioning schemes for concatenated_aa_complete.phy. The file partitions the 51,175 amino acid characters into 426 character sets, and defines the best substitution model for each character set. <b>10) concatenated_aa_reduced.phy:</b> a reduced concatenated amino acid dataset used for the maximum likelihood analysis by IQ-TREE v1.6.12, corresponding to concatenated_nt_reduced.phy. The file lists the sequences of 248 samples with 31,384 amino acid positions from 374 loci. Hyphens are used to represent gaps. <b>11) concatenated_aa_reduced_partition.nex:</b> the partitioning schemes for concatenated_aa_reduced.phy. The file partitions the 31,384 amino acid characters into 312 character sets, and defines the best substitution model for each character set. <b>12) Individual_gene_alignment.zip:</b> contains 426 FASTA files, each one is an alignment for a gene. Hyphens are used to represent gaps. These files were used to construct gene trees using IQ-TREE v1.6.12, followed by multispecies coalescent analysis using ASTRAL v 4.10.5 based the consensus trees with a minimum average bootstrap value of 70.

The effect of pesticide contamination on arthropod biomass and diversity in simulated prairie restorations depended on arthropod feeding guild (e.g., predator, herbivore, or pollinator). The pesticides used in this study were the neonicotinoid insecticide clothianidin and the phthalimide fungicide captan. This dataset includes two data files. The first contains information about the study sites ("plots") and pesticide treatments. The second contains information about arthropod biomass and morphospecies richness separated by feeding guild for each month-plot combination. R code in an R Markdown file for the analysis and data presentation in the associated publication is also provided. Detected effects included: predator biomass was 66% lower in plots treated with clothianidin, and this effect persisted across the growing season; the impact on herbivore biomass appeared to be inconsistent, with biomass being 51% lower with clothianidin in June but no detected difference in July or August; herbivore morphospecies richness was 12% lower in plots treated with both clothianidin and captain; pollinators appeared to be unaffected by clothianidin; and pollinator biomass increased by 71% when captan was applied to a plot.

This dataset contains experimental measurements used in the paper, "Ultra-sensitivity of Numerical Landscape Evolution Models to their Initial Conditions." (to be submitted). The data is taken from experimental runs in a miniature landscape model named the eXperimental Landscape Evolution (XLE) facility. In this facility, we complete five >24hr runs at 5 minute temporal resolution. Every five minutes, an planform image was capture, and a digital elevation model (DEM) was generated. For each run, images and a corresponding animation of images are documented. In addition,ASCII formatted DEMs along with color hillshade maps were generated. The hillshade map images were also made into an animation. This dataset is associated with the following publication: https://doi.org/10.1029/2019GL083305

The bee visitation data includes the percentage of each bee pollinator group in bee bowls and observed. The data are referenced in the article with the following citation: Bennett, A.B., Lovell, S.T. 2019. Landscape and local site variables differentially influence pollinators and pollination services in urban agricultural sites. Accepted for publication in: PLOS ONE.

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.

Genotype calls are provided for a collection of 583 Miscanthus sinensis clones across 1,108,836 loci mapped to version 7 of the Miscanthus sinensis reference genome. Sequence and alignment information for all unique RAD tags is also provided to facilitate cross-referencing to other genomes.

Dataset associated with "Hiding in plain sight: genetic confirmation of putative Louisiana Fatmucket Lampsilis hydiana in Illinois" as submitted to Freshwater Mollusk Biology and Conservation by Stodola et al. Images are from cataloged specimens from the Illinois Natural History Survey (INHS) Mollusk Collection in Champaign, Illinois that were used for genetic research. File names indicate the species as confirmed in Stodola et al. (i.e., Lampsilis siliquoidea or Lampsilis hydiana) followed by the INHS Mollusk Collection catalog number, followed by the individual specimen number, followed by shell view (interior or exterior). If no specimen number is noted in the file name, there is only one specimen for that catalog number. For example: Lsiliquoidea_46515_1_2_3_exterior. Images were created by photographing specimens on a metric grid in an OrTech Photo-e-Box Plus with a Nikon D610 single lens reflex camera using a 60mm lens. Post-processing of images (cropping, image rotation, and auto contrast) occurred in Adobe Photoshop and saved as TIFF files using no image compression, interleaved pixel order, and IBM PC Byte Order. One additional partial lot, INHS Mollusk Catalog No. 37059 (shown with both interior and exterior view in one image), is included for reference but was not genetically sequenced. A .csv file contains an index of all specimens photographed. SPECIES: species confirmed using genetic analyses GENE: cox1 or nad1 mitochondrial gene ACCESSION: GenBank accession number INHS CATALOG NO: Illinois Natural History Survey Mollusk Collection Catalog number WATERBODY: waterbody where specimen was collected PUTATIVE SPECIES: species determination based on morphological characters prior to genetic analysis Phylogenetic sequence data (.nex files) were aligned using BioEdit (Hall, T.A. 1999. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series 41:95-98.). Pertinent methodology for the analysis are contained within the manuscript submittal for Stodola et al. to Freshwater Mollusk Biology and Conservation. In these files, "N" is a standard symbol for an unknown base.

The included files were used to reconstruct the phylogeny of Coelidiinae using combined morphological and molecular data, estimate divergence times and reconstruct ancestral biogeographic areas as described in the manuscript submitted for publication. The file “Coelidiinae_dna_morph_combined.nex” is a text file in standard NEXUS format used by various phylogenetic analysis programs. This file includes the aligned and concatenated nucleotide sequences or five gene regions (mitochondrial COI and 16S, and nuclear 28S D-2, histone H3, histone H2A and wingless) indicated by standard “ACGT” nucleotide symbols with missing data indicated by “?”, and morphological character data as defined in Table S3 used in the analyses. The data partitions are indicated toward the end of the file by ranges of numbers (“charset Subset 1 – 4” for the DNA data and “charset morph” for the morphological characters) followed by commands for the phylogenetic analysis program MrBayes that specify the model settings for each data partition. Detailed data on species included (as rows) in the dataset, including collection localities and GenBank accession numbers are provided in the Table_S1_Specimen_information.csv file. The file "TablesS2-S4.pdf" lists the primers used for polymerase chain reaction amplification, the list of morphological character definitions, and the morphological character matrix. The file “RASP_Distribution.csv” contains a list of the species included in the phylogenetic dataset (first column) and a code (second column) indicating their distributions as follows: (A) Oriental, (B) Palaearctic, (C) Australian, (D) Afrotropical, (E) Neotropical, and (F) Nearctic. More than one letter indicates that the species occurs in more than one region. The file "infile_for_BEAST.txt" is the input file in XML format used for the molecular divergence time analysis using the program BEAST (Bayesian Evolutionary Analysis by Sampling Trees) as described in the Methods section of the manuscript. This file includes comments that document the steps of the analysis.

Copyright Review Management System renewals by year, data from Table 2 of the article "How Large is the ‘Public Domain’? A comparative Analysis of Ringer’s 1961 Copyright Renewal Study and HathiTrust CRMS Data."

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>

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.

The success of a bioenergy policy relies largely on the wide adoption of perennial energy crops at the farm scale. This study uses survey data to examine potential adoption decisions by farmers in the U.S. Midwest and the causal effects of various direct and indirect influencing factors, especially heterogeneous preferences of farmers. A Bayesian network (BN) model is developed to delineate the causal relationship between farmers adoption decisions and the influencing factors. We find a dominating role of economic factors and a non-negligible impact of non-economic factors, such as the perceived environmental benefits and the extent of familiarity with perennial energy crops. To examine the effect of heterogeneity in farmer preferences, we classify the surveyed farmers into four categories based on their attitudes toward the economic, social, and environmental dimensions of perennial energy crops. We identified statistically significant between-group differences in the responses of the four types of farmers to the various influencing factors. Our findings contribute to disentangling the complicated motivations that will influence perennial energy crop adoption decisions and provide implications for more targeted policy development that need to consider the heterogeneous drivers of farmer decisions about land use.

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.

These are text files downloaded from the Web of Science for the bibliographic analyses found in Zinnen et al. (2020) in Applied Vegetation Science. They represent the papers and reference lists from six expert-based indicator systems: Floristic Quality Assessment, hemeroby, naturalness indicator values (& social behaviors), Ellenberg indicator values, grassland utilization values, and urbanity indicator values. To examine data, download VOSviewer and see instructrions from van Eck & Waltman (2019) for how to upload data. Although we used bibliographic coupling, there are a number of other interesting bibliographic analyses you can use with these data (e.g., visualizing citations between journals from this set of documents). Note: There are two caveats to note about these data and Supplements 1 & 2 associated with our paper. First, there are some overlapping papers in these text files (i.e., raw data). When added individually, the papers sum to more than the numbers we give. However, when combined VOSviewer recognizes these as repeats, and matches the numbers we list in S1 and the manuscript. Second, we labelled the downloaded papers in S2 with their respective systems. In some cases, the labels do not completely match our counts listed in S1 and raw data. This is because some of these papers use another system, but were not captured in our systematic literature search (e.g., a paper may have used hemeroby, but was not picked up by WoS, so this paper is not listed as one of the 52 hemeroby papers).

This dataset contains all data used in the paper "Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids". The dataset includes genotypes and phenotypic data from two autotetraploid species Miscanthus sacchariflorus and Vaccinium corymbosum that was used used for genome wide association studies and genomic prediction and the scripts used in the analysis. In this V2, 2 files have the raw data are added: "Miscanthus_sacchariflorus_RADSeq.vcf" is the VCF file with the raw SNP calls of the Miscanthus sacchariflorus data used for genotype calling using the 6 genotype calling methods. "Blueberry_data_read_depths.RData" is the a RData file with the read depth data that was used for genotype calling in the Blueberry dataset.

During the transformation of wild-type (WT) Arabidopsis thaliana, a T-DNA containing OLEOSIN-GFP (OLE1-GFP) was inserted by happenstance within the GBSS1 gene, resulting in significant reduction in amylose and increase in leaf oil content in the transgenic line (OG). The synergistic effect on oil accumulation of combining gbss1 with the expression of OLE1-GFP was confirmed by transforming an independent gbss1 mutant (GABI_914G01) with OLE1-GFP. The resulting OLE1-GFP/gbss1 transgenic lines showed higher leaf oil content than the individual OLE1-GFP/WT or single gbss1 mutant lines. Further stacking of the lipogenic factors WRINKLED1, Diacylglycerol O-Acyltransferase (DGAT1), and Cys-OLEOSIN1 (an engineered sesame OLEOSIN1) in OG significantly elevated its oil content in mature leaves to 2.3% of dry weight, which is 15 times higher than that in WT Arabidopsis. Inducible expression of the same lipogenic factors was shown to be an effective strategy for triacylglycerol (TAG) accumulation without incurring growth, development, and yield penalties.

Self-citation analysis data based on PubMed Central subset (2002-2005) ---------------------------------------------------------------------- Created by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, and Vetle Torvik on April 5th, 2018 ## Introduction This is a dataset created as part of the publication titled: Mishra S, Fegley BD, Diesner J, Torvik VI (2018) Self-Citation is the Hallmark of Productive Authors, of Any Gender. PLOS ONE. It contains files for running the self citation analysis on articles published in PubMed Central between 2002 and 2005, collected in 2015. The dataset is distributed in the form of the following tab separated text files: * Training_data_2002_2005_pmc_pair_First.txt (1.2G) - Data for first authors * Training_data_2002_2005_pmc_pair_Last.txt (1.2G) - Data for last authors * Training_data_2002_2005_pmc_pair_Middle_2nd.txt (964M) - Data for middle 2nd authors * Training_data_2002_2005_pmc_pair_txt.header.txt - Header for the data * COLUMNS_DESC.txt file - Descriptions of all columns * model_text_files.tar.gz - Text files containing model coefficients and scores for model selection. * results_all_model.tar.gz - Model coefficient and result files in numpy format used for plotting purposes. v4.reviewer contains models for analysis done after reviewer comments. * README.txt file ## Dataset creation Our experiments relied on data from multiple sources including properitery data from [Thompson Rueter's (now Clarivate Analytics) Web of Science collection of MEDLINE citations](<a href="https://clarivate.com/products/web-of-science/databases/">https://clarivate.com/products/web-of-science/databases/</a>). Author's interested in reproducing our experiments should personally request from Clarivate Analytics for this data. However, we do make a similar but open dataset based on citations from PubMed Central which can be utilized to get similar results to those reported in our analysis. Furthermore, we have also freely shared our datasets which can be used along with the citation datasets from Clarivate Analytics, to re-create the datased used in our experiments. These datasets are listed below. If you wish to use any of those datasets please make sure you cite both the dataset as well as the paper introducing the dataset. * 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> * Citation data from PubMed Central (original paper includes additional citations from Web of Science) * Author-ity 2009 dataset: - Dataset citation: <a href="https://doi.org/10.13012/B2IDB-4222651_V1">Torvik, Vetle I.; Smalheiser, Neil R. (2018): Author-ity 2009 - PubMed author name disambiguated dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4222651_V1</a> - Paper citation: <a href="https://doi.org/10.1145/1552303.1552304">Torvik, V. I., & Smalheiser, N. R. (2009). Author name disambiguation in MEDLINE. ACM Transactions on Knowledge Discovery from Data, 3(3), 1–29. https://doi.org/10.1145/1552303.1552304</a> - Paper citation: <a href="https://doi.org/10.1002/asi.20105">Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2004). A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), 140–158. https://doi.org/10.1002/asi.20105</a> * Genni 2.0 + Ethnea for identifying author gender and ethnicity: - Dataset citation: <a href="https://doi.org/10.13012/B2IDB-9087546_V1">Torvik, Vetle (2018): Genni + Ethnea for the Author-ity 2009 dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9087546_V1</a> - Paper citation: <a href="https://doi.org/10.1145/2467696.2467720">Smith, B. N., Singh, M., & Torvik, V. I. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. In Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries - JCDL ’13. ACM Press. https://doi.org/10.1145/2467696.2467720</a> - Paper citation: <a href="http://hdl.handle.net/2142/88927">Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geo-coded 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</a> * MapAffil for identifying article country of affiliation: - Dataset citation: <a href="https://doi.org/10.13012/B2IDB-4354331_V1">Torvik, Vetle I. (2018): MapAffil 2016 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4354331_V1</a> - Paper citation: <a href="http://doi.org/10.1045/november2015-torvik">Torvik VI. MapAffil: A Bibliographic Tool for Mapping Author Affiliation Strings to Cities and Their Geocodes Worldwide. D-Lib magazine : the magazine of the Digital Library Forum. 2015;21(11-12):10.1045/november2015-torvik</a> * IMPLICIT journal similarity: - Dataset citation: <a href="https://doi.org/10.13012/B2IDB-4742014_V1">Torvik, Vetle (2018): Author-implicit journal, MeSH, title-word, and affiliation-word pairs based on Author-ity 2009. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4742014_V1</a> * Novelty dataset for identify article level novelty: - Dataset citation: <a href="https://doi.org/10.13012/B2IDB-5060298_V1">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</a> - Paper citation: <a href="https://doi.org/10.1045/september2016-mishra"> 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</a> - Code: <a href="https://github.com/napsternxg/Novelty">https://github.com/napsternxg/Novelty</a> * Expertise dataset for identifying author expertise on articles: * Source code provided at: <a href="https://github.com/napsternxg/PubMed_SelfCitationAnalysis">https://github.com/napsternxg/PubMed_SelfCitationAnalysis</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 Self-citation analysis data based on PubMed Central subset (2002-2005) by Shubhanshu Mishra, Brent D. Fegley, Jana Diesner, 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/PubMed_SelfCitationAnalysis">https://github.com/napsternxg/PubMed_SelfCitationAnalysis</a>.

Author-ity 2009 baseline dataset. Prepared by Vetle Torvik 2009-12-03 The dataset comes in the form of 18 compressed (.gz) linux text files named authority2009.part00.gz - authority2009.part17.gz. The total size should be ~17.4GB uncompressed. &bull; How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in July 2009. A total of 19,011,985 Article records and 61,658,514 author name instances. Each instance of an author name is uniquely represented by the PMID and the position on the paper (e.g., 10786286_3 is the third author name on PMID 10786286). Thus, each cluster is represented by a collection of author name instances. The instances were first grouped into "blocks" by last name and first name initial (including some close variants), and then each block was separately subjected to clustering. Details are described in <i>Torvik, V., & Smalheiser, N. (2009). Author name disambiguation in MEDLINE. ACM Transactions On Knowledge Discovery From Data, 3(3), doi:10.1145/1552303.1552304</i> <i>Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2005). A Probabilistic Similarity Metric for Medline Records: A Model for Author Name Disambiguation. Journal Of The American Society For Information Science & Technology, 56(2), 140-158. doi:10.1002/asi.20105</i> Note that for Author-ity 2009, some new predictive features (e.g., grants, citations matches, temporal, affiliation phrases) and a post-processing merging procedure were applied (to capture name variants not capture during blocking e.g. matches for subsets of compound last name matches, and nicknames with different first initial like Bill and William), and a temporal feature was used -- this has not yet been written up for publication. &bull; How accurate is the 2009 dataset (compared to 2006 and 2009)? The recall reported for 2006 of 98.8% has been much improved in 2009 (because common last name variants are now captured). Compared to 2006, both years 2008 and 2009 overall seem to exhibit a higher rate of splitting errors but lower rate of lumping errors. This reflects an overall decrease in prior probabilites -- possibly because e.g. a) new prior estimation procedure that avoid wild estimates (by dampening the magnitude of iterative changes); b) 2008 and 2009 included items in Pubmed-not-Medline (including in-process items); and c) and the dramatic (exponential) increase in frequencies of some names (J. Lee went from ~16,000 occurrences in 2006 to 26,000 in 2009.) Although, splitting is reduced in 2009 for some special cases like NIH funded investigators who list their grant number of their papers. Compared to 2008, splitting errors were reduced overall in 2009 while maintaining the same level of lumping errors. &bull; What is the format of the dataset? The cluster summaries for 2009 are much more extenstive than the 2008 dataset. Each line corresponds to a predicted author-individual represented by cluster of author name instances and a summary of all the corresponding papers and author name variants (and if there are > 10 papers in the cluster, an identical summary of the 10 most recent papers). Each cluster has a unique Author ID (which is uniquely identified by the PMID of the earliest paper in the cluster and the author name position. The summary has the following tab-delimited fields: 1. blocks separated by '||'; each block may consist of multiple lastname-first initial variants separated by '|' 2. prior probabilities of the respective blocks separated by '|' 3. Cluster number relative to the block ordered by cluster size (some are listed as 'CLUSTER X' when they were derived from multiple blocks) 4. Author ID (or cluster ID) e.g., bass_c_9731334_2 represents a cluster where 9731334_2 is the earliest author name instance. Although not needed for uniqueness, the id also has the most frequent lastname_firstinitial (lowercased). 5. cluster size (number of author name instances on papers) 6. name variants separated by '|' with counts in parenthesis. Each variant of the format lastname_firstname middleinitial, suffix 7. last name variants separated by '|' 8. first name variants separated by '|' 9. middle initial variants separated by '|' ('-' if none) 10. suffix variants separated by '|' ('-' if none) 11. email addresses separated by '|' ('-' if none) 12. range of years (e.g., 1997-2009) 13. Top 20 most frequent affiliation words (after stoplisting and tokenizing; some phrases are also made) with counts in parenthesis; separated by '|'; ('-' if none) 14. Top 20 most frequent MeSH (after stoplisting; "-") with counts in parenthesis; separated by '|'; ('-' if none) 15. Journals with counts in parenthesis (separated by "|"), 16. Top 20 most frequent title words (after stoplisting and tokenizing) with counts in parenthesis; separated by '|'; ('-' if none) 17. Co-author names (lowercased lastname and first/middle initials) with counts in parenthesis; separated by '|'; ('-' if none) 18. Co-author IDs with counts in parenthesis; separated by '|'; ('-' if none) 19. Author name instances (PMID_auno separated '|') 20. Grant IDs (after normalization; "-" if none given; separated by "|"), 21. Total number of times cited. (Citations are based on references extracted from PMC). 22. h-index 23. Citation counts (e.g., for h-index): PMIDs by the author that have been cited (with total citation counts in parenthesis); separated by "|" 24. Cited: PMIDs that the author cited (with counts in parenthesis) separated by "|" 25. Cited-by: PMIDs that cited the author (with counts in parenthesis) separated by "|" 26-47. same summary as for 4-25 except that the 10 most recent papers were used (based on year; so if paper 10, 11, 12... have the same year, one is selected arbitrarily)

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>

The file “Fla.fasta”, comprising 10526 positions, is the concatenated amino acid alignments of 51 orthologues of 182 bacterial strains. It was used for the maximum likelihood and maximum parsimony analyses of Flavobacteriales. Bacterial species names and strains were used as the sequence names, host names of insect endosymbionts were shown in brackets. The file “16S.fasta” is the alignment of 233 bacterial 16S rRNA sequences. It contains 1455 positions and was used for the maximum likelihood analysis of flavobacterial insect endosymbionts. The names of endosymbiont strains were replaced by the name of their hosts. In addition to the species names, National Center for Biotechnology Information (NCBI) accession numbers were also indicated in the sequence names (e.g., sequence “Cicadellidae_Deltocephalinae_Macrostelini_Macrosteles_striifrons_AB795320” is the 16S rRNA of Macrosteles striifrons (Cicadellidae: Deltocephalinae: Macrostelini) with a NCBI accession number AB795320). The file “Sulcia_pep.fasta” is the concatenated amino acid alignments of 131 orthologues of “Candidatus Sulcia muelleri” (Sulcia). It contains 41970 positions and presents 101 Sulcia strains and 3 Blattabacterium strains. This file was used for the maximum likelihood analysis of Sulcia. The file “Sulcia_nucleotide.fasta” is the concatenated nucleotide alignment corresponding to the sequences in “Sulcia_pep.fasta” but also comprises the alignment of 16S rRNA. It has 127339 positions and was used for the maximum likelihood and maximum parsimony analyses of Sulcia. Individual gene alignments (16S rRNA and 131 orthologues of Sulcia and Blattabacterium) are deposited in the compressed file “individual_gene_alignments.zip”, which were used to construct gene trees for multispecies coalescent analysis. The names of Sulcia strains were replaced by the name of their hosts in “Sulcia_pep.fasta”, “Sulcia_nucleotide.fasta” and the files in “individual_gene_alignments.zip”. In all the alignment files, gaps are indicated by “-”.

If you use this dataset, please cite the IJRR data paper (bibtex is below). We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The data is divided into subsets, which can be downloaded individually. Video previews are available on Youtube: https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset. Images ====== Raw --- The raw images are stored in the cam0 and cam1 directories in bmp format. They are bayered images that need to be debayered and undistorted before they are used. The camera parameters for these images can be found in camchain-imucam.yaml. Note that the camera intrinsics describe a 1600x1200 resolution image, so the focal length and center pixel coordinates must be scaled by 0.5 before they are used. The distortion coefficients remain the same even for the scaled images. The camera to imu tranformation matrix is also in this file. cam0/ refers to the left camera, and cam1/ refers to the right camera. Rectified --------- Stereo rectified, undistorted, row-aligned, debayered images are stored in the rectified/ directory in the same way as the raw images except that they are in png format. The params.yaml file contains the projection and rotation matrices necessary to use these images. The resolution of these parameters do not need to be scaled as is necessary for the raw images. params.yml ---------- The stereo rectification parameters. R0,R1,P0,P1, and Q correspond to the outputs of the OpenCV stereoRectify function except that 1s and 2s are replaced by 0s and 1s, respectively. R0: The rectifying rotation matrix of the left camera. R1: The rectifying rotation matrix of the right camera. P0: The projection matrix of the left camera. P1: The projection matrix of the right camera. Q: Disparity to depth mapping matrix T_cam_imu: Transformation matrix for a point in the IMU frame to the left camera frame. camchain-imucam.yaml -------------------- The camera intrinsic and extrinsic parameters and the camera to IMU transformation usable with the raw images. T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame. distortion_coeffs: lens distortion coefficients using the radial tangential model. intrinsics: focal length x, focal length y, principal point x, principal point y resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled. T_cn_cnm1: Transformation matrix from the right camera to the left camera. Sensors ------- Here, each message in name.csv is described ###rawimus### time # GPS time in seconds message name # rawimus acceleration_z # m/s^2 IMU uses right-forward-up coordinates -acceleration_y # m/s^2 acceleration_x # m/s^2 angular_rate_z # rad/s IMU uses right-forward-up coordinates -angular_rate_y # rad/s angular_rate_x # rad/s ###IMG### time # GPS time in seconds message name # IMG left image filename right image filename ###inspvas### time # GPS time in seconds message name # inspvas latitude longitude altitude # ellipsoidal height WGS84 in meters north velocity # m/s east velocity # m/s up velocity # m/s roll # right hand rotation about y axis in degrees pitch # right hand rotation about x axis in degrees azimuth # left hand rotation about z axis in degrees clockwise from north ###inscovs### time # GPS time in seconds message name # inscovs position covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz m^2 attitude covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz deg^2 velocity covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz (m/s)^2 ###bestutm### time # GPS time in seconds message name # bestutm utm zone # numerical zone utm character # alphabetical zone northing # m easting # m height # m above mean sea level Camera logs ----------- The files name.cam0 and name.cam1 are text files that correspond to cameras 0 and 1, respectively. The columns are defined by: unused: The first column is all 1s and can be ignored. software frame number: This number increments at the end of every iteration of the software loop. camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped. camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds. PC timestamp: This is the PC time of arrival of the image. name.kml -------- The kml file is a mapping file that can be read by software such as Google Earth. It contains the recorded GPS trajectory. name.unicsv ----------- This is a csv file of the GPS trajectory in UTM coordinates that can be read by gpsbabel, software for manipulating GPS paths. @article{doi:10.1177/0278364917751842, author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, title ={The Visual–Inertial Canoe Dataset}, journal = {The International Journal of Robotics Research}, volume = {37}, number = {1}, pages = {13-20}, year = {2018}, doi = {10.1177/0278364917751842}, URL = {https://doi.org/10.1177/0278364917751842}, eprint = {https://doi.org/10.1177/0278364917751842} }

# 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.