University Library, University of Illinois at Urbana-Champaign

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the <a href="https://docs.google.com/forms/d/e/1FAIpQLSf-J937V6I4sMSxQt7gR3SIbUASR26KXxqSurrkBvlF-CIQnQ/viewform?usp=pp_url"><b>Archer Access Request Form</b></a> so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the <a href="https://forms.gle/6eA2yJUGFMtj5swY7"><b>Archer User Feedback Form</b></a>. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this <a href="https://groups.webservices.illinois.edu/subscribe/123172"><b>form</b></a> to subscribe to it. <b>Citation Guidelines:</b> 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2023. Global News Index and Extracted Features Repository [codebook], v1.2.0. Champaign, IL: University of Illinois. June. XX. doi:10.13012/B2IDB-5649852_V5 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2023. Global News Index and Extracted Features Repository [database], v1.2.0. Champaign, IL: University of Illinois. Jun. XX. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V5 *NOTE: V4 is suppressed and V5 is replacing V4 with updated ‘Archer’ documents.

This dataset is associated with a larger manuscript published in 2022 in the Illinois Natural History Survey Bulletin that summarized the Fishes of Champaign County project from 2012-2015. With data spanning over 120 years, the Fishes of Champaign County is a comprehensive, long-term investigation into the changing fish communities of east-central Illinois. Surveys first occurred in Champaign County in the late 1880s (40 sites), with subsequent surveys in 1928–1929 (125 sites), 1959–1960 (143 sites), and 1987–1988 (141 sites). Between 2012 and 2015, we resampled 122 sites across Champaign County. The combined data from these five surveys have produced a unique perspective into not only the fish communities of the region, but also insight into in-stream habitat changes during the past 120 years. The dataset is in Microsoft Access format, with five data tables, one for each time period surveyed. Field names are self-explanatory, with some variation in data types collected during different surveys as follows: Forbes & Richardson (1880s) collected presence/absence only. Thompson & Hunt (1928-1929) collected abundance only, Larimore & Smith (1959-1960) collected length and weight for some samples, but only presence/absence at others. In some cases, fish of the same species were weighed in bulk, with the fields “LOW” and “HIGH” indicating the lower and upper limits of total length in the batch, and weight indicating the gross weight of all fish in the batch. Larimore and Bayley (1987-1988) collected length and weight for all surveys, and Sherwood and Stein (2012-2015) collected length and weight for all surveys except for cases where extremely abundant single species where subsampled. Lengths are reported in millimeters, and weight in grams. Two lookup tables provide information about species codes used in the data tables and sample site location and notes.

This dataset contains four real-world sub-datasets with data embedded into Poincare ball models, including Olsson's single-cell RNA expression data, CIFAR10, Fashion-MNIST and mini-ImageNet. Each sub-dataset has two corresponding files: one is the data file, the other one is the pre-computed reference points for each class in the sub-dataset. Please refer to our paper (https://arxiv.org/pdf/2109.03781.pdf) and codes (https://github.com/thupchnsky/PoincareLinearClassification) for more details.

Results of RT-LAMP reactions for influenza A virus diagnostic development.

A stochastic domination analysis model was developed to examine the effect that emerging carbon markets can have on the spatially varying returns and risk profiles of bioenergy crops relative to conventional crops. The code is written in MATLAB, and includes the calculated output. See the README file for instructions to run the code.

Raw MD simulation trajectory, input and configuration files, SEM current data, and experimental raw data accompanying the publication, "Electrical recognition of the twenty proteinogenic amino acids using an aerolysin nanopore". README.md contains a description of all associated files.

Example data for https://github.com/chenlabUIUC/UsiNet The data contains computer simulated and experimental tilting series (or sinograms) of gold nanoparticles. Two training data examples are provided: 1. simulated_data.zip 2. experimental_data.zip In each zip folder, we include an image_data.zip and a training_data.zip. The former is for viewing and only the latter is needed for model training. For more details, please refer to our GitHub repository.

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 through the XSEDE operational period, which ended August 31, 2022. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation. Because the XSEDE program has ended, the allocation_award_history file includes all allocations activity initiated via XSEDE processes through August 31, 2022. The Resource Providers and successor program to XSEDE agreed to honor all project allocations made during XSEDE. Thus, allocation awards that extend beyond the end of XSEDE may not reflect all activity that may ultimately be part of the project award. Similarly, allocation usage data only reflects usage reported through August 31, 2022, and may not reflect all activity that may ultimately be conducted by projects that were active beyond XSEDE.

This dataset includes structural MRI head scans of 32 piglets, at 28 days of age, scanned at the University of Illinois. The dataset also includes manually drawn brain masks of each of the piglets. The dataset also includes brain masks that were generated automatically using Region-Based Convolutional Neural Networks (Mask R-CNN), trained on the manually drawn brain masks.

This is the data used in the paper "Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data". A preprint may be found at https://doi.org/10.48550/arXiv.2212.11367 Code from the Github repository https://github.com/adtonks/mosquito_GNN can be used with the data here to reproduce the paper's results. v1.0.0 of the code is also archived at https://doi.org/10.5281/zenodo.7897830

This dataset includes microclimate species distribution models at a ~3 m2 spatial resolution and free-air temperature species distribution models at ~0.85 km2 spatial resolution for three plethodontid salamander species (Demognathus wrighti, Desmognathus ocoee, and Plethodon jordani) across Great Smoky Mountains National Park. We also include heatmaps representing the differences between microclimate and free-air species distribution models and polygon layers representing the fragmented habitat for each species' predicted range. All datasets include predictions for 2010, 2030, and 2050.

Tab-separated value (TSV) file. 14745 data rows. Each data row represents publication metadata as retrieved from Crossref (http://crossref.org) 2023-04-05 when searching for retracted publications. Each row has the following columns: Index - Our index, starting with 0. DOI - Digital Object Identifier (DOI) for the publication Year - Publication year associated with the DOI. URL - Web location associated with the DOI. Title - Title associated with the DOI. May be blank. Author - Author(s) associated with the DOI. Journal - Publication venue (journal, conference, ...) associated with the DOI RetractionYear - Retraction Year associated with the DOI. May be blank. Category - One or more categories associated with the DOI. May be blank. Our search was via the Crossref REST API and searched for: Update_type=( 'retraction', 'Retraction', 'retracion', 'retration', 'partial_retraction', 'withdrawal','removal')

This is a simulated sequence dataset generated using INDELible and processed via a sequence fragmentation procedure.

Supplemental data sets for the Manuscript entitled " Assembly of wood-inhabiting archaeal, bacterial and fungal communities along a salinity gradient: common taxa are broadly distributed but locally abundant in preferred habitats"

Data associated with the manuscript "Eastern banded killifish (Fundulus diaphanus diaphanus) in Lake Michigan and connected watersheds: the invasion of a non-native subspecies" by Jordan H. Hartman, Jeremy S. Tiemann, Joshua L. Sherwood, Philip W. Willink, Kurt T. Ash, Mark A. Davis, and Eric R. Larson. For this project, we sampled 109 locations in Lake Michigan and connected waters and found 821 total banded killifish. Using mitochondrial DNA analysis, we found 31 eastern and 25 western haplotypes which split our banded killifish into 422 eastern banded killifish and 398 western banded killifish. This dataset provides the sampling locations, banded killifish haplotypes, frequency of those haplotypes per location, accession numbers in GenBank, and the associated mitochondrial DNA sequences.

STEM images of kinks in α-In2Se3, DFT calculation of bending of α-In2Se3, PFM on as exfoliated and controllably bend α-In2Se3

Use of cellulosic biofuels from non-feedstocks are modeled using the BEPAM (Biofuel and Environmental Policy Analysis Model) model to quantifying the uncertainties about induced land use change effects, net greenhouse gas saving potential, and economic costs. The code is in GAMS, general algebraic modeling language. NOTE: Column 3 is titled "BAU" in "merged_BAU.gdx", "merged_RFS.gdx", and "merged_CEM.gdx", but contains "RFS" data in "merged_RFS.gdx" and "CEM" data in "merged_CEM.gdx".

This dataset contains the full data used in the paper titled "Enabling High Precision Gradient Index Control in Subsurface Multiphoton Lithography," available at https://doi.org/10.1021/acsphotonics.2c01950 . The data used for Table 1 can be found in the dataset for the related Figure 8. Some supplemental figures' data can be found in the main figures data: Figure S2's data is contained in Figure 6. Figure S4 and Table S1 data is derived from Figure 6. Figure S9 is derived from Figure 7. Figure S10 is contained in Figure 7. Figure S12 is derived from Figure 6 and the Python code prism-fringe-analysis. Figures without a data file named after them do not have any data affiliated with them and are purely graphical representations.

Sentences and citation contexts identified from the PubMed Central open access articles ---------------------------------------------------------------------- The dataset is delivered as 24 tab-delimited text files. The files contain 720,649,608 sentences, 75,848,689 of which are citation contexts. The dataset is based on a snapshot of articles in the XML version of the PubMed Central open access subset (i.e., the PMCOA subset). The PMCOA subset was collected in May 2019. The dataset is created as described in: Hsiao TK., & Torvik V. I. (manuscript) OpCitance: Citation contexts identified from the PubMed Central open access articles. <b>Files</b>: • A_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with A. • B_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with B. • C_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with C. • D_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with D. • E_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with E. • F_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with F. • G_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with G. • H_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with H. • I_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with I. • J_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with J. • K_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with K. • L_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with L. • M_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with M. • N_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with N. • O_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with O. • P_p1_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 1). • P_p2_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with P (part 2). • Q_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with Q. • R_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with R. • S_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with S. • T_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with T. • UV_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with U or V. • W_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with W. • XYZ_journal_IntxtCit.tsv – Sentences and citation contexts identified from articles published in journals with journal titles starting with X, Y or Z. Each row in the file is a sentence/citation context and contains the following columns: • pmcid: PMCID of the article • pmid: PMID of the article. If an article does not have a PMID, the value is NONE.  • location: The article component (abstract, main text, table, figure, etc.) to which the citation context/sentence belongs.  • IMRaD: The type of IMRaD section associated with the citation context/sentence. I, M, R, and D represent introduction/background, method, results, and conclusion/discussion, respectively; NoIMRaD indicates that the section type is not identifiable.  • sentence_id: The ID of the citation context/sentence in the article component • total_sentences: The number of sentences in the article component.  • intxt_id: The ID of the citation. • intxt_pmid: PMID of the citation (as tagged in the XML file). If a citation does not have a PMID tagged in the XML file, the value is "-". • intxt_pmid_source: The sources where the intxt_pmid can be identified. Xml represents that the PMID is only identified from the XML file; xml,pmc represents that the PMID is not only from the XML file, but also in the citation data collected from the NCBI Entrez Programming Utilities. If a citation does not have an intxt_pmid, the value is "-".  • intxt_mark: The citation marker associated with the inline citation. • best_id: The best source link ID (e.g., PMID) of the citation. • best_source: The sources that confirm the best ID. • best_id_diff: The comparison result between the best_id column and the intxt_pmid column. • citation: A citation context. If no citation is found in a sentence, the value is the sentence.  • progression: Text progression of the citation context/sentence.  <b>Supplementary Files</b> • PMC-OA-patci.tsv.gz – This file contains the best source link IDs for the references (e.g., PMID). Patci [1] was used to identify the best source link IDs. The best source link IDs are mapped to the citation contexts and displayed in the *_journal IntxtCit.tsv files as the best_id column. Each row in the PMC-OA-patci.tsv.gz file is a citation (i.e., a reference extracted from the XML file) and contains the following columns: • pmcid: PMCID of the citing article. • pos: The citation's position in the reference list. • fromPMID: PMID of the citing article. • toPMID: Source link ID (e.g., PMID) of the citation. This ID is identified by Patci. • SRC: The sources that confirm the toPMID. • MatchDB: The origin bibliographic database of the toPMID. • Probability: The match probability of the toPMID. • toPMID2: PMID of the citation (as tagged in the XML file). • SRC2: The sources that confirm the toPMID2. • intxt_id: The ID of the citation. • journal: The first letter of the journal title. This maps to the *_journal_IntxtCit.tsv files. • same_ref_string: Whether the citation string appears in the reference list more than once. • DIFF: The comparison result between the toPMID column and the toPMID2 column. • bestID: The best source link ID (e.g., PMID) of the citation. • bestSRC: The sources that confirm the best ID. • Match: Matching result produced by Patci. [1] Agarwal, S., Lincoln, M., Cai, H., & Torvik, V. (2014). Patci – a tool for identifying scientific articles cited by patents. GSLIS Research Showcase 2014. http://hdl.handle.net/2142/54885 • intxt_cit_license_fromPMC.tsv – This file contains the CC licensing information for each article. The licensing information is from PMC's file lists [2], retrieved on June 19, 2020, and March 9, 2023. It should be noted that the license information for 189,855 PMCIDs is <b>NO-CC CODE</b> in the file lists, and 521 PMCIDs are absent in the file lists. The absence of CC licensing information does not indicate that the article lacks a CC license. For example, PMCID: 6156294 (<b>NO-CC CODE</b>) and PMCID: 6118074 (absent in the PMC's file lists) are under CC-BY licenses according to their PDF versions of articles. The intxt_cit_license_fromPMC.tsv file has two columns: • pmcid: PMCID of the article. • license: The article’s CC license information provided in PMC’s file lists. The value is nan when an article is not present in the PMC’s file lists. [2] https://www.ncbi.nlm.nih.gov/pmc/tools/ftp/ • Supplementary_File_1.zip – This file contains the code for generating the dataset.

This datasets provide basis of our analysis in the paper - Potential Impacts on Ozone and Climate from a Proposed Fleet of Supersonic Aircraft. All datasets here can be categorized into emission data and model output data (WACCM). All the model simulations (background and perturbation) were run to steady-state and only the datasets used in analysis are archived here.

Curated networks and clustering output from the manuscript: Well-Connected Communities in Real-World Networks https://arxiv.org/abs/2303.02813

This data set is related to the SoyFACE experiments, which are open-air agricultural climate change experiments that have been conducted since 2001. The fumigation experiments take place at the SoyFACE farm and facility in Champaign County, Illinois during the growing season of each year, typically between June and October. - The <i>"SoyFACE Plot Information 2001 to 2021"</i> file contains information about each year of the SoyFACE experiments, including the fumigation treatment type (CO2, O3, or a combination treatment), the crop species, the plots (also referred to as 'rings' and labeled with numbers between 2 and 31) used in each experiment, important experiment dates, and the target concentration levels or 'setpoints' for CO2 and O3 in each experiment. - This data set includes files with minute readings of the fumigation levels (<i>"SoyFACE 1-Minute Fumigation Data Files"</i> folder) from the SoyFACE experiments. The <i>"Soyface 1-Minute Fumigation Data Files"</i> folder contains sub-folders for each year of the experiments, each of which contains sub-folders for each ring used in that year's experiments. This data set also includes hourly data files for the fumigation experiments (<i>"SoyFACE Hourly Fumigation Data Files"</i> folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments (<i>"Hourly Weather and Ambient Data Files"</i> folder). The ambient CO2 and O3 data are collected at SoyFACE, and the weather data are collected from the SURFRAD and WARM weather stations located near the SoyFACE farm. - The <i>"Fumigation Target Percentages"</i> file shows how much of the time the CO2 and O3 fumigation levels are within a 10 or 20 percent margin of the target levels when the fumigation system is turned on. - The <i>"Matlab Files"</i> folder contains custom code (Aspray, E.K.) that was used to clean the <i>"SoyFACE 1-Minute Fumigation Data"</i> files and to generate the <i>"SoyFACE Hourly Fumigation Data"</i> and <i>"Fumigation Target Percentages"</i> files. Code information can be found in the <i>"SoyFACE Hourly Fumigation Data Explanation"</i> file. - Finally, the <i>" * Explanation"</i> files contain information about the column names, units of measurement, and other pertinent information for each data file. *<b>NOTE:</b> We have identified some files in the “SoyFACE 1-Minute Fumigation Data Files” folder in our SoyFACE data set submission that were not downloaded properly - the files were present in the folder, but the actual files were empty. V3 ensures that there are no longer any empty files in the data set.

This dataset contains the historical and future (SSP3 and RCP7.0) CESM climate simulations used in the article "Large humidity effects on urban heat exposure and cooling challenges under climate change" (upcoming). Further details about these simulations can be found in the article. This dataset documents the monthly mean projections of air temperature, wet-bulb temperature, precipitation, relative humidity, and numerous other climatic variables for 2000-2009 (for the historical run) and for 2015-2100 (for the future projection under SSP3-RCP7). This dataset may be useful for urban planners, climate scientists, and decision-makers interested in changes in urban and rural climate under climate change.

These data represent the raw data from the paper “Evaluating the ability of wetland mitigation banks to replace plant species lost from destroyed wetlands” published in Journal of Applied Ecology in 2023 by Stephen C. Tillman and Jeffrey W. Matthews.

This dataset includes mass spectrometry, library screening, and gas chromatography data used for creating a high-throughput screening in metabolic engineering.