University Library, University of Illinois at Urbana-Champaign

This is the data set from the published manuscript 'Vertebrate scavenger guild composition and utilization of carrion in an East Asian temperate forest' by Inagaki et al.

This data set explores the effect of the cyanobacterial gene ictB on photosynthesis in sorghum, under both normal greenhouse growing temperatures (32 C / 25 C) and during and after an 8 day chilling stress (10 C / 5 C). IctB is a cyanobacterial gene of unknown function, which was initially thought to be involved in inorganic carbon transport into cells. While ictB is known now not to be an independently active carbon transporter in its own right, it may play a role in passive diffusion of metabolites. This transgene was introduced into sorghum by the lab of Thomas Clemente, through Agrobacterium mediated transformation, alone and in combination with the tomato sedoheptulose-1,7-bisphosphatase (SBPase) gene. Eleven events (six double construct and five single construct ictB) were involved in this study. SBPase was included because some previous experiments in C3 species and some previous modeling work, as well as its position at a metabolic branch point, indicates it plays a role as a control point for photosynthesis. A chilling treatment was included because chilling is one of the most serious ecological factors limiting the range of C4 species. Data includes gene expression, metabolomics (at normal growing temperature), SBPase enzyme activity, biomass and photosynthetic traits at both warm temperature and during and after chilling stress. ----------------- EXPLANATORY NOTES FOR ICTB/SBPASE SORGHUM MANUSCRIPT Data are organized into 10 worksheets, representing an expected 10 tables that will serve a supplementary role in the final publication. These include data on gene expression, metabolomics (at normal growing temperature), SBPase enzyme activity, biomass and photosynthetic traits at both warm temperature and during and after chilling stress. <i><b>Tables are as follows:</i></b> 1. Event_Code: for Table S1. Event codes for events and constructs. Two constructs were generated for this study, and numerous transgenic “events” (i.e. independent transformations) were carried out for each construct. A construct represents the actual vector which was introduced into the plants (complete with promoter, gene of interest, marker gene, etc.) while an event represents a single successful introduction of the transgene. Events are uniquely labeled with letter and number strings but also with a four-digit number for ease of reference, this table explains which event corresponds to each four-digit number. 2. Photosynthetic_Data: for Table S2. Photosynthetic data at greenhouse growing temperature, for ictB single construct, ictB/SBPase double construct, and wild type lines. Five ictB and six ictB/SBPase events were included. Greenhouse growing temperature was approximately 32 °C and 25 °C night. Photosynthetic parameters were measured using a Licor 6400-XT, and included parameters related to carbon dioxide uptake, water loss, and chlorophyll fluorescence. 3. Chilling_Treatment: for Table S3. Photosynthetic response to chilling treatment, for ictB single construct, and wild type lines. Four ictB events were included. Chilling treatment lasted approximately 8 days and began either 3.5 or 5.5 weeks after transplanting the plants (chilling was done in two batches). Chilling treatment involved temperatures of 10 °C day / 7 °C night in growth chambers. Photosynthetic parameters were measured at several time points during and after the chilling treatment, were measured using a Licor 6400-XT, and included parameters related to carbon dioxide uptake, water loss, and chlorophyll fluorescence. 4. SBPase_Activity: for Table S4. SBPase activity in double construct plants. These data measure in vitro substrate-saturated activity of SBPase in desalted extracts from leaf tissues, at 25 °C. Units are micromoles of SBP processed per second per m2 of leaf tissue. Five ictB/SBPase events were included. 5. 2014_gene_exp: for Table S5. Gene expression in 2014 experiment (units of cycle times). These data measure cycle times to threshold, relative to reference genes, for expression of ictB and SBPase. Six ictB single construct events and five ictB/SBPase double construct events were included. Cycle times to threshold relative to reference genes (ΔCT) are inversely related to number of transcripts relative to reference genes, as follows: ΔCT = -log2([NictB]/[Nreference])/[1 + log2b] where b = efficiency of replication. 6. 2016_gene_exp: for Table S5. Gene expression in 2016 experiment (units of cycle times). These data measure cycle times to threshold, relative to reference genes, for expression of ictB and SBPase. Six ictB single construct events and five ictB/SBPase double construct events were included. Cycle times to threshold relative to reference genes (ΔCT) are inversely related to number of transcripts relative to reference genes, as follows: ΔCT = -log2([NictB]/[Nreference])/[1 + log2b] where b = efficiency of replication. 7. Metabolites: for Table S7. Levels of 267 metabolites in leaf tissue. Four ictB single construct events and four ictB/SBPase double construct events were included in these analyses. Metabolites were measured in methanol-extracted samples, either by liquid chromatography / mass spectrometry or by gas chromatography / mass spectrometry, and were compared between events on a relative basis. As quantification was relative to wild type rather than on an absolute basis, no units are included. 8. Metabolite_F_values: for Table S8. F values for effects of ictB, SBPase (in cases where the model was better with a SBPase effect) and event. These analyses are done for each metabolite included in Table S7, and show effects of the explanatory variables ictB, SBPase, and individual event. 9. Biomass_2020: for Table S9. Biomass and grain yield at harvest, for ictB, ictB/SBPase and wild type sorghum plants in spring 2020. Four ictb/SBPase double construct and four ictB single construct events were included. 10. Biomass_2017: for Table S10. Biomass and grain yield at harvest, in chilled and non-chilled sorghum plants containing the ictB transgene (along with wild type controls) in fall 2017. Four ictB single construct events were included. Chilling treatment involved temperatures of 10 °C day / 7 °C night in growth chambers. <i><b>All the variables in the file are explained as below:</i></b> o Type (IctB-SBPase and IctB). This refers to whether a plant is wild type, single construct (contains only the ictB transgene) or double construct (contains both the ictB and SBPase transgenes). o Code: these codes are shorter labels to refer to each transgene event for the sake of convenience. o Alternate_Code: these codes are shorter labels to refer to each transgene event for the sake of convenience. o Event Number: these are unique labels for each transgenic events. o Construct Number: these are labels for each transgenic construct (either the ictB single construct or the ictB/SBPase double construct). o year (i): this refers to the year in which the study was conducted (2014, 2016, 2017, or 2020) o transgene or Transgenic: whether the transgene was present o construct or Type : whether the ictB or the ictB/SBPase construct was present (double, single, wildtype): o temp: leaf temperature during the measurement o A: carbon assimilation rate, in μmol m-2 s-1 o gs: stomatal conductance, in mol m-2 s-1 o CI: intercellular carbon dioxide concentration, in parts per million or μL L-1 o fvfm:FV’/FM’ (maximal potential photosystem II quantum yield under light adapted conditions), dimensionless ratio o phipsill: ΦPSII (maximal potential photosystem II quantum yield under light adapted conditions), dimensionless ratio o qP: photochemical quenching, i.e. ratio of ΦPSII to FV’/FM’ , dimensionless ratio o iwue: intrinsic water use efficiency, i.e. ratio of carbon assimilation rate to stomatal conductance, in units of μmol mol-1 o event: individual transgenic / transformation event o Vmax: substrate-saturated in vitro activity of the SBPase enzyme, in μmol m-2 s-1 o ID: identification number of sample o ΔCT1: difference in cycle times to threshold during gene expression (quantitative PCR) assay, between ictB and the reference gene GAPDH, in units of cycles o ΔCT2: cycle times to threshold during gene expression (quantitative PCR) assay, between SBPase and the reference gene GAPDH, in units of cycles o GAPDH: cycle times to threshold for the reference gene GAPDH (glyceraldehyde phosphate dehydrogenase) o IctB: cycle times to threshold for the gene of interest ictB o SBPase: cycle times to threshold for the gene of interest SBPase o v1 to v267 represent individual metabolite (see the heading immediately above the labels v1, v2, etc.). Variables v268-v272 refer to total (summed) metabolite levels for particular pathways of interest. o leaf: Leaf and stem dry biomass (in grams) o seed: Seedhead dry biomass (in grams) o biomass: Total (leaf, stem + seed head) dry biomass (in grams) o harvind: ratio of seed head dry biomass to total dry biomass o treatment (chilled and nonchilled): “Chilled” plants were grown under warm greenhouse conditions (32 °C day / 25 °C night) for 6 or 8 weeks, then switched to chilling temperatures under growth chamber conditions (10 °C / 7 °C night) for 8 days, and were then returned to greenhouse growing conditions. -----------------

These data obtained from the peer-reviewed literature and a public database depict the geographic expansion of the black-legged tick (Ixodes scapularis) and human cases of Lyme disease in the midwestern U.S. <b><i>Note</b></i>: There was an omission from the first version (V1) of the data set that required us to update the data. Specifically, we failed to include the data from the article "Caporale DA, Johnson CM, Millard BJ. 2005 Presence of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae) in Southern Kettle Moraine State Forest, Wisconsin, and characterization of strain W97F51. J. Med. Entomol. 42, 457–472". In the second version (V2) of the data, this omission is corrected.

This is the dataset that accompanies the paper titled "A Dual-Frequency Radar Retrieval of Snowfall Properties Using a Neural Network", submitted for peer review in August 2020. Please see the github for the most up-to-date data after the revision process: https://github.com/dopplerchase/Chase_et_al_2021_NN Authors: Randy J. Chase, Stephen W. Nesbitt and Greg M. McFarquhar Corresponding author: Randy J. Chase (randyjc2@illinois.edu) Here we have the data used in the manuscript. Please email me if you have specific questions about units etc. 1) DDA/GMM database of scattering properties: base_df_DDA.csv This is the combined dataset from the following papers: Leinonen & Moisseev, 2015; Leinonen & Szyrmer, 2015; Lu et al., 2016; Kuo et al., 2016; Eriksson et al., 2018. The column names are D: Maximum dimension in meters, M: particle mass in grams kg, sigma_ku: backscatter cross-section at ku in m^2, sigma_ka: backscatter cross-section at ka in m^2, sigma_w: backscatter cross-section at w in m^2. The first column is just an index column. 2) Synthetic Data used to train and test the neural network: Unrimed_simulation_wholespecturm_train_V2.nc, Unrimed_simulation_wholespecturm_test_V2.nc This was the result of combining the PSDs and DDA/GMM particles randomly to build the training and test dataset. 3) Notebook for training the network using the synthetic database and Google Colab (tensorflow): Train_Neural_Network_Chase2020.ipynb This is the notebook used to train the neural network. 4)Trained tensorflow neural network: NN_6by8.h5 This is the hdf5 tensorflow model that resulted from the training. You will need this to run the retrieval. 5) Scalers needed to apply the neural network: scaler_X_V2.pkl, scaler_y_V2.pkl These are the sklearn scalers used in training the neural network. You will need these to scale your data if you wish to run the retrieval. 6) <b>New in this version</b> - Example notebook of how to run the trained neural network on Ku- Ka- band observations. We showed this with the 3rd case in the paper: Run_Chase2021_NN.ipynb 7) <b>New in this version</b> - APR data used to show how to run the neural network retrieval: Chase_2021_NN_APR03Dec2015.nc The data for the analysis on the observations are not provided here because of the size of the radar data. Please see the GHRC website (<a href="https://ghrc.nsstc.nasa.gov/home/">https://ghrc.nsstc.nasa.gov/home/</a>) if you wish to download the radar and in-situ data or contact me. We can coordinate transferring the exact datafiles used. The GPM-DPR data are avail. here: <a href="http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05">http://dx.doi.org/10.5067/GPM/DPR/GPM/2A/05</a>

Dataset includes temperature data (local average April daily temperatures), first egg dates and reproductive output of Prothonotary Warblers breeding in southernmost Illinois, USA. Also included are arrival dates for warblers returning to breeding grounds from wintering grounds, and global temperature anomaly data for comparison with local temperatures. These data were used in the manuscript entitled "Warmer April Temperatures on Breeding Grounds Promote Earlier Nesting in a Long-Distance Migratory Bird, the Prothonotary Warbler" published in Frontiers in Ecology and Evolution. A rich text file is included with explanations of each variable in the dataset.

This repository includes scripts and datasets for Chapter 6 of my PhD dissertation, " Supertree-like methods for genome-scale species tree estimation," that had not been published previously. This chapter is based on the article: Molloy, E.K. and Warnow, T. "FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models." Bioinformatics, In press. https://doi.org/10.1093/bioinformatics/btaa444. The results presented in my PhD dissertation differ from those in the Bioinformatics article, because I re-estimated species trees using FastMulRF and MulRF on the same datasets in the original repository (https://doi.org/10.13012/B2IDB-5721322_V1). To re-estimate species trees, (1) a seed was specified when running MulRF, and (2) a different script (specifically preprocess_multrees_v3.py from https://github.com/ekmolloy/fastmulrfs/releases/tag/v1.2.0) was used for preprocessing gene trees (which were then given as input to MulRF and FastMulRFS). Note that this preprocessing script is a re-implementation of the original algorithm for improved speed (a bug fix also was implemented). Finally, it was brought to my attention that the simulation in the Bioinformatics article differs from prior studies, because I scaled the species tree by 10 generations per year (instead of 0.9 years per generation, which is ~1.1 generations per year). I re-simulated datasets (true-trees-with-one-gen-per-year-psize-10000000.tar.gz and true-trees-with-one-gen-per-year-psize-50000000.tar.gz) using 0.9 years per generation to quantify the impact of this parameter change (see my PhD dissertation or the supplementary materials of Bioinformatics article for discussion).

This data set shows how density effects have an important influence on mixing at a small river confluence. The data consist of results of simulations using a detached eddy simulation model.

The Allan Lab has published a Fluidigm pipeline online. This is the url: https://github.com/HPCBio/allan-fluidigm-pipeline. This url includes a tutorial for running the pipeline. However it does not have test datasets yet. This tarball hosted at the Illinois Data Bank is the dataset that completes the github tutorial. It includes inputs (custom database of tick pathogens and fluidigm raw reads) and output files (tables of samples with taxonomic classifications).

This dataset contains BEPAM model code and input data to replicate the outcomes for "The Economic and Environmental Costs and Benefits of the Renewable Fuel Standard". The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output.gms. (BEPAM-Social cost model-ERL.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

This dataset contains the publication record of 6429 computer science researchers collected from the Microsoft Academic dataset provided through their Knowledge Service API (http://bit.ly/microsoft-data).

A 30 year record of the vegetation in sample plots in a woodland in the Chicago area. The changes in these plots over time show how ecological restoration can yield dramatic results.

We are releasing the tracing dataset of four microservice benchmarks deployed on our dedicated Kubernetes cluster consisting of 15 heterogeneous nodes. The dataset is not sampled and is from selected types of requests in each benchmark, i.e., compose-posts in the social network application, compose-reviews in the media service application, book-rooms in the hotel reservation application, and reserve-tickets in the train ticket booking application. The four microservice applications come from [DeathStarBench](https://github.com/delimitrou/DeathStarBench) and [Train-Ticket](https://github.com/FudanSELab/train-ticket). The performance anomaly injector is from [FIRM](https://gitlab.engr.illinois.edu/DEPEND/firm.git). The dataset was preprocessed from the raw data generated in FIRM's tracing system. The dataset is separated by on which microservice component is the performance anomaly located (as the file name suggests). Each dataset is in CSV format and fields are separated by commas. Each line consists of the tracing ID and the duration (in 10^(-3) ms) of each component. Execution paths are specified in `execution_paths.txt` in each directory.

Video footage of an Eastern Box Turtle (Terrapene carolina carolina) partially predating a Field Sparrow nest (Spizella pusilla) at 0845 h on the 31 of May 2020. Please note that the date on the video footage is incorrect due to user error, but the time is correct.

This dataset consists of various input data that are used in the GAMS model. All the data are in the format of .inc which can be read within GAMS or Notepad. Main data sources include: acreage data (acre), crop budget data ($/acre), crop yield data (e.g. bushel/acre), Soil carbon sequestration data (KgCO2/ha/yr). Model details can be found in the "Assessing the Additional Carbon Savings with Biofuel" and GAMS model package. ## File Description (1) GAMS Model.zip: This includes all the input files and scripts for running the model (2) Table*.csv: These files include the data from the tables in the manuscript (3) Figure2_3_4.csv: This contains the data used to create the figures in the manuscript (4) BaselineResults.csv: This includes a summary of the model results. (5) SensitivityResults_*.csv: Model results from the various sensitivity analyses performed (6) LUC_emission.csv: land use change emissions by crop reporting district for changes of pasturelands to annual crops.

Data on permanent plots at Fortuna and the Panama Canal Watershed, Republic of Panama, containing counts and percent of trees with one or more multiple stems >10cm diameter, with and without palms. Accompanying environmental data includes elevation, precipitation, soil type and soil chemical variables (pH, total N, NO3, NO4, resin P, mehlich Ca, K and Mg.

Data in this spreadsheet presents basic information on Cahokia, Mound 72 shell artifacts. This includes taxonomic identifications, provenience, and bead measurements. There are five tabs: 1. Raw data; 2. Disk bead measurements; 3. Columella bead measurements; 4. Data on cups and pendants; and, 5. Information on whole shell beads.

Raw gas exchange data for photosynthetic induction in 6 rice accession flag leaves. Photosynthetic induction and point measurements were made at ambient [CO2]. Two accessions (AUS 278 and IR64) were selected to screen in greater detail in which photosynthetic induction was measured at six [CO2].

This repository contains the datasets and corresponding results for the paper "MAGUS: Multiple Sequence Alignment using Graph Clustering". The Datasets.zip archive contains the ROSE, balibase, Gutell, and RNASim datasets used in our experiments. The Results.zip archive contains the outputs of running our methods against these datasets. Datasets used: ROSE: 10 simulated nucleotide model conditions from the SATe paper, each with 20 replicates, and with 1000 sequences per replicate. The ROSE datasets were originally taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/sate-i">https://sites.google.com/eng.ucsd.edu/datasets/alignment/sate-i</a> RNASim: This is a collection of simulated nucleotide datasets that were generated under a model of evolution that reflects selection due to RNA structural constraints. We sampled 20 subsets of 1000 sequences each, as well as 10 subsets of 10000 each, by randomly sampling from the original million-sequence RNASim dataset. Gutell: 16S.M, 16S.3, 16S.T, 16S.B.ALL: Four biological nucleotide datasets from the Comparative Ribosomal Website (CRW) with cleaned reference alignments from SATe. Since PASTA is restricted to datasets without sequence length heterogeneity, these were modified to remove sequences that deviate by more than 20% from the median length. The scrubbed datasets range from 740 to 24,246 sequences. The pre-screened 16S datasets were taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/16s23s">https://sites.google.com/eng.ucsd.edu/datasets/alignment/16s23s</a> BAliBASE: We use eight BAliBASE amino acid datasets used in the PASTA paper. As above, we remove outlier sequences, which leaves us with sizes ranging from 195 to 732 sequences. The pre-screened Balibase datasets were taken from <a href="https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp">https://sites.google.com/eng.ucsd.edu/datasets/alignment/pastaupp</a>

Data extracted from Text, Tables and Figures of publications in summarizing crop responses to Free-Air CO2 Elevation (FACE)

This dataset contains R codes used to produce the figures submitted in the manuscript titled "Understanding the multifaceted geospatial software ecosystem: a survey approach". The raw survey data used to populate these charts cannot be shared due to the survey consent agreement.

Restriction site-associated DNA sequencing (RAD-seq) data from 643 Miscanthus accessions from a diversity panel, including 613 Miscanthus sacchariflorus, three M. sinensis, and 27 M. xgiganteus. DNA was digested with PstI and MspI, and single-end Illumina sequencing was performed adjacent to the PstI site. Variant and genotype calling was performed with TASSEL-GBSv2, using the Miscanthus sinensis v7.1 reference genome from Phytozome 12 (https://phytozome.jgi.doe.gov). Additional ploidy-aware genotype calling was performed by polyRAD v1.1.

Data are from a long-term fire manipulation experiment in the Missouri Ozarks, USA. Data include the raw, annual ring-width increment (rwl), basal area increment (BAI), population-level annual growth resistance (Drs) and resilience (Drl) to drought, intrinsic water use efficiency values (WUEi) and oxygen isotopic composition of individual radial growth rings (δ18O) from southern red oak (Quercus falcata) and post oak (Q. stellata) trees. ---------------------- TITLE: Data for "Sixty-five years of fire manipulation reveals climate and fire interact to determine growth rates of Quercus spp." ---------------------- FILE OVERVIEW: This dataset contains four (4) CSV files as described below: Refsland_et_al_ECS20-0465_BAI.csv: annual basal area increment between 1948-2015 for trees across the fire manipulation experiment Refsland_et_al_ECS20-0465_DroughtIndices.csv: population-level drought resistance and resilience of trees during each target drought period Refsland_et_al_ECS20-0465_WUEi.csv: carbon isotope indicators of drought stress for trees across the fire manipulation experiment Refsland_et_al_ECS20-0465_d18Or.csv: oxygen isotope indicators of drought stress for trees across the fire manipulation experiment ---------------------- VARIABLE EXPLANATION: All the variables in those four files are explained as below: treeID: unique character string that identifies subject tree block: integer (1, 2) that identifies the study block plot: integer (1-12) that identifies the plot nested within each study block trt: character string (Annual, Control, Periodic) that identifies the fire treatment of a given plot species: character string (Quercus falcata, Quercus stellata) that identifies species of subject tree year: integer (1948-2015) that identifies the dated year of each tree ring rwl_mm: numerical value representing the annual tree ring-width, in mm bai_cm2: numerical value representing the annual basal area increment, in cm2 timeperiod: integer value (1953, 1964, 2007, 2012) representing the periods encompassing target dry and wet years Drs_2yr: numerical value representing the drought resistance, defined as the population-level annual growth of trees during drought years relative to pre-drought years for a given time period Drl_2yr: numerical value representing the drought resilience, defined as the population-level annual growth of trees following drought years relative to pre-drought years for a given time period stand_ba_m2ha: numerical value representing the total basal area of a given plot, in m2 per ha stand_density_stems_ha: numerical value representing the total stem density of a given plot, in stems per ha pool: numerical value (1-40) identifying the set of tree ring samples pooled for analysis. Samples were pooled by block, plot, year and species period: integer value (1953, 1964, 1980, 2007, 2012) representing the periods encompassing target dry and wet years type: character string (Dry, Wet) indicating the water availability of a given year d13C: numerical value representing the carbon isotopic composition of radial growth rings within a given sample pool, in per mil WUEi: numerical value representing the annual intrinsic water use efficiency of radial growth rings within a given sample pool d18O: numerical value representing the oxygen isotopic composition of radial growth rings within a given sample pool, in per mil

This dataset contains BEPAM model code and input data to the replicate the results for "Assessing the Returns to Land and Greenhouse Gas Savings from Producing Energy Crops on Conservation Reserve Program Land." The dataset consists of: (1) The replication codes and data for the BEPAM model. The code file is named as output_0213-2020_Complete_daycent-agversion-[rental payment level]%_[biomass price].gms. (BEPAM-CRP model-Sep2020.zip) (2) Simulation results from the BEPAM model (BEPAM_Simulation_Results.csv) * Item (1) is in GAMS format. Item (2) is in text format.

Citation context annotation. This dataset is a second version (V2) and part of the supplemental data for Jodi Schneider, Di Ye, Alison Hill, and Ashley Whitehorn. (2020) "Continued post-retraction citation of a fraudulent clinical trial report, eleven years after it was retracted for falsifying data". Scientometrics. In press, DOI: 10.1007/s11192-020-03631-1 Publications were selected by examining all citations to the retracted paper Matsuyama 2005, and selecting the 35 citing papers, published 2010 to 2019, which do not mention the retraction, but which mention the methods or results of the retracted paper (called "specific" in Ye, Di; Hill, Alison; Whitehorn (Fulton), Ashley; Schneider, Jodi (2020): Citation context annotation for new and newly found citations (2006-2019) to retracted paper Matsuyama 2005. University of Illinois at Urbana-Champaign. <a href="https://doi.org/10.13012/B2IDB-8150563_V1">https://doi.org/10.13012/B2IDB-8150563_V1</a> ). The annotated citations are second-generation citations to the retracted paper Matsuyama 2005 (RETRACTED: Matsuyama W, Mitsuyama H, Watanabe M, Oonakahara KI, Higashimoto I, Osame M, Arimura K. Effects of omega-3 polyunsaturated fatty acids on inflammatory markers in COPD. Chest. 2005 Dec 1;128(6):3817-27.), retracted in 2008 (Retraction in: Chest (2008) 134:4 (893) <a href="https://doi.org/10.1016/S0012-3692(08)60339-6">https://doi.org/10.1016/S0012-3692(08)60339-6<a/> ). <b>OVERALL DATA for VERSION 2 (V2)</b> FILES/FILE FORMATS Same data in two formats: 2010-2019 SG to specific not mentioned FG.csv - Unicode CSV (preservation format only) - same as in V1 2010-2019 SG to specific not mentioned FG.xlsx - Excel workbook (preferred format) - same as in V1 Additional files in V2: 2G-possible-misinformation-analyzed.csv - Unicode CSV (preservation format only) 2G-possible-misinformation-analyzed.xlsx - Excel workbook (preferred format) <b>ABBREVIATIONS: </b> 2G - Refers to the second-generation of Matsuyama FG - Refers to the direct citation of Matsuyama (the one the second-generation item cites) <b>COLUMN HEADER EXPLANATIONS </b> File name: 2G-possible-misinformation-analyzed. Other column headers in this file have same meaning as explained in V1. The following are additional header explanations: Quote Number - The order of the quote (citation context citing the first generation article given in "FG in bibliography") in the second generation article (given in "2G article") Quote - The text of the quote (citation context citing the first generation article given in "FG in bibliography") in the second generation article (given in "2G article") Translated Quote - English translation of "Quote", automatically translation from Google Scholar Seriousness/Risk - Our assessment of the risk of misinformation and its seriousness 2G topic - Our assessment of the topic of the cited article (the second generation article given in "2G article") 2G section - The section of the citing article (the second generation article given in "2G article") in which the cited article(the first generation article given in "FG in bibliography") was found FG in bib type - The type of article (e.g., review article), referring to the cited article (the first generation article given in "FG in bibliography") FG in bib topic - Our assessment of the topic of the cited article (the first generation article given in "FG in bibliography") FG in bib section - The section of the cited article (the first generation article given in "FG in bibliography") in which the Matsuyama retracted paper was cited

# 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.