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Illinois Data Bank Dataset Search Results

Dataset Search Results

published: 2018-03-01
 
The data set consists of Illumina sequences derived from 48 sediment samples, collected in 2015 from Lake Michigan and Lake Superior for the purpose of inventorying the fungal diversity in these two lakes. DNA was extracted from ca. 0.5g of sediment using the MoBio PowerSoil DNA isolation kits following the Earth Microbiome protocol. PCR was completed with the fungal primers ITS1F and fITS7 using the Fluidigm Access Array. The resulting amplicons were sequenced using the Illumina Hi-Seq2500 platform with rapid 2 x 250nt paired-end reads. The enclosed data sets contain the forward read files for both primers, both fixed-header index files, and the associated map files needed to be processed in QIIME. In addition, enclosed are two rarefied OTU files used to evaluate fungal diversity. All decimal latitude and decimal longitude coordinates of our collecting sites are also included. File descriptions: Great_lakes_Map_coordinates.xlsx = coordinates of sample sites QIIME Processing ITS1 region: These are the raw files used to process the ITS1 Illumina reads in QIIME. ***only forward reads were processed GL_ITS1_HW_mapFile_meta.txt = This is the map file used in QIIME. ITS1F_Miller_Fludigm_I1_fixedheader.fastq = Index file from Illumina. Headers were fixed to match the forward reads (R1) file in order to process in QIIME ITS1F_Miller_Fludigm_R1.fastq = Forward Illumina reads for the ITS1 region. QIIME Processing ITS2 region: These are the raw files used to process the ITS2 Illumina reads in QIIME. ***only forward reads were processed GL_ITS2_HW_mapFile_meta.txt = This is the map file used in QIIME. ITS7_Miller_Fludigm_I1_Fixedheaders.fastq = Index file from Illumina. Headers were fixed to match the forward reads (R1) file in order to process in QIIME ITS7_Miller_Fludigm_R1.fastq = Forward Illumina reads for the ITS2 region. Resulting OTU Table and OTU table with taxonomy ITS1 Region wahl_ITS1_R1_otu_table.csv = File contains Representative OTUs based on ITS1 region for all the R1 data and the number of each OTU found in each sample. wahl_ITS1_R1_otu_table_w_tax.csv = File contains Representative OTUs based on ITS1 region for all the R1 and the number of each OTU found in each sample along with taxonomic determination based on the following database: sh_taxonomy_qiime_ver7_97_s_31.01.2016_dev ITS2 Region wahl_ITS2_R1_otu_table.csv = File contains Representative OTUs based on ITS2 region for all the R1 data and the number of each OTU found in each sample. wahl_ITS2_R1_otu_table_w_tax.csv = File contains Representative OTUs based on ITS2 region for all the R1 data and the number of each OTU found in each sample along with taxonomic determination based on the following database: sh_taxonomy_qiime_ver7_97_s_31.01.2016_dev Rarified illumina dataset for each ITS Region ITS1_R1_nosing_rare_5000.csv = Environmental parameters and rarefied OTU dataset for ITS1 region. ITS2_R1_nosing_rare_5000.csv = Environmental parameters and rarefied OTU dataset for ITS2 region. Column headings: #SampleID = code including researcher initials and sequential run number BarcodeSequence = LinkerPrimerSequence = two sequences used CTTGGTCATTTAGAGGAAGTAA or GTGARTCATCGAATCTTTG ReversePrimer = two sequences used GCTGCGTTCTTCATCGATGC or TCCTCCGCTTATTGATATGC run_prefix = initials of run operator Sample = location code, see thesis figures 1 and 2 for mapped locations and Great_lakes_Map_coordinates.xlsx for exact coordinates. DepthGroup = S= shallow (50-100 m), MS=mid-shallow (101-150 m), MD=mid-deep (151-200 m), and D=deep (>200 m)" Depth_Meters = Depth in meters Lake = lake name, Michigan or Superior Nitrogen % Carbon % Date = mm/dd/yyyy pH = acidity, potential of Hydrogen (pH) scale SampleDescription = Sample or control X = sequential run number OTU ID = Operational taxonomic unit ID
keywords: Illumina; next-generation sequencing; ITS; fungi
published: 2018-03-08
 
This dataset was developed to create a census of sufficiently documented molecular biology databases to answer several preliminary research questions. Articles published in the annual Nucleic Acids Research (NAR) “Database Issues” were used to identify a population of databases for study. Namely, the questions addressed herein include: 1) what is the historical rate of database proliferation versus rate of database attrition?, 2) to what extent do citations indicate persistence?, and 3) are databases under active maintenance and does evidence of maintenance likewise correlate to citation? An overarching goal of this study is to provide the ability to identify subsets of databases for further analysis, both as presented within this study and through subsequent use of this openly released dataset.
keywords: databases; research infrastructure; sustainability; data sharing; molecular biology; bioinformatics; bibliometrics
published: 2018-07-28
 
This dataset presents a citation analysis and citation context analysis used in Linh Hoang, Frank Scannapieco, Linh Cao, Yingjun Guan, Yi-Yun Cheng, and Jodi Schneider. Evaluating an automatic data extraction tool based on the theory of diffusion of innovation. Under submission. We identified the papers that directly describe or evaluate RobotReviewer from the list of publications on the RobotReviewer website <http://www.robotreviewer.net/publications>, resulting in 6 papers grouped into 5 studies (we collapsed a conference and journal paper with the same title and authors into one study). We found 59 citing papers, combining results from Google Scholar on June 05, 2018 and from Scopus on June 23, 2018. We extracted the citation context around each citation to the RobotReviewer papers and categorized these quotes into emergent themes.
keywords: RobotReviewer; citation analysis; citation context analysis
published: 2024-05-07
 
This dataset builds on an existing dataset which captures artists’ demographics who are represented by top tier galleries in the 2016–2017 New York art season (Case-Leal, 2017, https://web.archive.org/web/20170617002654/http://www.havenforthedispossessed.org/) with a census of reviews and catalogs about those exhibitions to assess proportionality of media coverage across race and gender. The readme file explains variables, collection, relationship between the datasets, and an example of how the Case-Leal dataset was transformed. The ArticleDataset.csv provides all articles with citation information as well as artist, artistic identity characteristic, and gallery. The ExhibitionCatalog.csv provides exhibition catalog citation information for each identified artist.
keywords: diversity and inclusion; diversity audit; contemporary art; art exhibitions; art exhibition reviews; exhibition catalogs; magazines; newspapers; demographics
published: 2024-12-05
 
Data consists of RNA expression, tuber mass, photosynthetic capacity and diurnal CO2 assimilation calculations, potato tuber nutrient content, photorespiratory metabolite analysis and meteorological data to support the increase in yield and thermotolerance observed in potato plants with an introduce photorespiratory bypass. Data was collected between 2019-2024 at University of Illinois at Urbana-Champaign, IL, USA.
keywords: Photorespiratory bypass; photosynthesis; photorespiration; food security; potato
published: 2016-07-22
 
Datasets and R scripts relating to the manuscript "Ecological characteristics and in situ genetic associations for yield-component traits of wild Miscanthus from eastern Russia" published in Annals of Botany, 10.1093/aob/mcw137. Field data, including collection locations, physical and ecological information for each location, and plant phenotypes relating to biomass are included. Genetic data in this repository include single nucleotide polymorphisms (SNPs) derived from restriction site-associated DNA sequencing (RAD-seq), as well as plastid microsatellites. A file is also included listing the DNA sequences of all RAD-seq markers generated to-date by the Sacks lab, including those from this publication.
keywords: Miscanthus sacchariflorus; Miscanthus sinensis; Russia; germplasm; RAD-seq; SNP
published: 2017-05-01
 
Indianapolis Int'l Airport to Urbana: Sampling Rate: 2 Hz Total Travel Time: 5901534 ms or 98.4 minutes Number of Data Points: 11805 Distance Traveled: 124 miles via I-74 Device used: Samsung Galaxy S6 Date Recorded: 2016-11-27 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously as a single trip, no stop was made for gas along the way making this a very long continuous dataset. It starts in the parking lot of the Indianapolis International Airport and continues directly towards a gas station on Lincoln Avenue in Urbana, IL. There are a couple parts of the trip where the phones orientation had to be changed because my navigation cut out. These times are easy to account for based on Orientation X/Y/Z change. I would also advise cutting out the first couple hundred points or the points leading up to highway speed. The phone was mounted in the cupholder in the front seat of the car.
keywords: smartphone; sensor; driving; accelerometer; gyroscope; magnetometer; gps; nmea; barometer; satellite
published: 2017-02-28
 
Leesburg, VA to Indianapolis, Indiana: Sampling Rate: 0.1 Hz Total Travel Time: 31100007 ms or 518 minutes or 8.6 hours Distance Traveled: 570 miles via I-70 Number of Data Points: 3112 Device used: Samsung Galaxy S4 Date Recorded: 2017-01-15 Parameters Recorded: * ACCELEROMETER X (m/s²) * ACCELEROMETER Y (m/s²) * ACCELEROMETER Z (m/s²) * GRAVITY X (m/s²) * GRAVITY Y (m/s²) * GRAVITY Z (m/s²) * LINEAR ACCELERATION X (m/s²) * LINEAR ACCELERATION Y (m/s²) * LINEAR ACCELERATION Z (m/s²) * GYROSCOPE X (rad/s) * GYROSCOPE Y (rad/s) * GYROSCOPE Z (rad/s) * LIGHT (lux) * MAGNETIC FIELD X (microT) * MAGNETIC FIELD Y (microT) * MAGNETIC FIELD Z (microT) * ORIENTATION Z (azimuth °) * ORIENTATION X (pitch °) * ORIENTATION Y (roll °) * PROXIMITY (i) * ATMOSPHERIC PRESSURE (hPa) * Relative Humidity (%) * Temperature (F) * SOUND LEVEL (dB) * LOCATION Latitude * LOCATION Longitude * LOCATION Altitude (m) * LOCATION Altitude-google (m) * LOCATION Altitude-atmospheric pressure (m) * LOCATION Speed (kph) * LOCATION Accuracy (m) * LOCATION ORIENTATION (°) * Satellites in range * GPS NMEA * Time since start in ms * Current time in YYYY-MO-DD HH-MI-SS_SSS format Quality Notes: There are some things to note about the quality of this data set that you may want to consider while doing preprocessing. This dataset was taken continuously but had multiple stops to refuel (without the data recording ceasing). This can be removed by parsing out all data that has a speed of 0. The mount for this dataset was fairly stable (as can be seen by the consistent orientation angle throughout the dataset). It was mounted tightly between two seats in the back of the vehicle. Unfortunately, the frequency for this dataset was set fairly low at one per ten seconds.
keywords: smartphone; sensor; driving; accelerometer; gyroscope; magnetometer; gps; nmea; barometer; satellite; temperature; humidity
published: 2024-01-31
 
This dataset contains: field study design parameters, plant performance metrics, and nitrogen cycling rates associated with a field experiment that compared nitrification rates between maize lines with and without nitrification inhibition loci nitrogen fixation rates with with and without a nitrogen fixing inoculant product. The overarching goal was to evaluate nitrogen fixation by a diazotroph inoculant and retention of nitrogen in the rhizosphere via a novel nitrification inhibition phenotype of maize.
keywords: maize; microbiome; nitrogen cycling; nitrification; nitrogen fixation
published: 2025-01-01
 
Raw data from a survey of para-veterinary workers in Pakistan regarding knowledge, attitudes, and practices around ticks and tick-borne diseases. Between March and August 2023, we conducted a web-based survey among para-veterinarians recruited via email, text message, and face-to-face conversations.
keywords: ticks; survey; tick-borne disease; para-veterinary workers
published: 2019-06-13
 
This lexicon is the expanded/enhanced version of the Moral Foundation Dictionary created by Graham and colleagues (Graham et al., 2013). Our Enhanced Morality Lexicon (EML) contains a list of 4,636 morality related words. This lexicon was used in the following paper - please cite this paper if you use this resource in your work. Rezapour, R., Shah, S., & Diesner, J. (2019). Enhancing the measurement of social effects by capturing morality. Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, MN. In addition, please consider citing the original MFD paper: <a href="https://doi.org/10.1016/B978-0-12-407236-7.00002-4">Graham, J., Haidt, J., Koleva, S., Motyl, M., Iyer, R., Wojcik, S. P., & Ditto, P. H. (2013). Moral foundations theory: The pragmatic validity of moral pluralism. In Advances in experimental social psychology (Vol. 47, pp. 55-130)</a>.
keywords: lexicon; morality
published: 2024-12-17
 
This repository contains precipitation spectra from a Parsivel-2 disdrometer deployed at Lancaster High School, Lancaster, NY, as well as a MRR-2 radar deployed at the same site. The site was located at 42.9299° N, 78.6708° W. Parsivel data were converted to netCDF using the pyDSD python package. MRR-2 spectra are raw from the manufacturer's software. The Parsivel and MRR-2 data include periods collected during November 2022 as described in the paper.
keywords: snowfall; disdrometer; spectra; micro rain radar; Doppler
published: 2024-12-20
 
All data presented in the manuscript published in the Journal of Geophysical Research-Biogeosciences by Stuchiner et al. 2025, "Hot or not? An evaluation of methods for identifying hot moments of nitrous oxide emissions from soils." This includes hourly N2O flux measurements from 20 autochambers from May 2022 to April 2023 in a maize field in central Illinois, and various metrics used to assess hot moments that are evaluated in the manuscript. Note that chamber 5 for each sampling node is sampled from a deep soil collar (50 cm depth) that excludes roots for the purpose of measuring heterotrophic respiration rates.
keywords: nitrous oxide; maize; hot moments; outlier detection; soil emissions
published: 2024-09-26
 
This dataset is from a study of a simulated angling tournament livewell holding in June of 2023 on Largemouth Bass (Micropterus nigricans) on Clinton Lake, Illinois. Fish were collected via electrofishing, weighed, measured and assessed for physical injury prior to receiving a commercially available cull tag and being placed in a simulated livewell. After a six hour livewell holding period, fish were removed from the livewell assessed for physical injury and then assessed for reflex action mortality predictors prior to being placed in a net pen for 3 days of observation. This dataset includes, weights, total lengths, physical injury scores, and reflex action mortality predictor scores for Largemouth Bass and water quality parameters of livewells and the lake in net pens.
keywords: sport fish conservation; fisheries management; high-grading; stringer
published: 2024-10-25
 
This is a reference package to be used with the TIPP3 software for abundance profiling of metagenomic reads sampled from a microbial community. TIPP3 software: https://github.com/c5shen/TIPP3 Usage: 1. unzip the file to a local directory (will get a folder named "tipp3-refpkg"). 2. use with TIPP3 software: `tipp3.py -r [path/to/tipp3-refpkg] [other parameters]`
keywords: TIPP3; abundance profile; reference database; taxonomic identification
published: 2024-12-12
 
This dataset supports the implementation described in the manuscript "Breaking the Barrier of Human-Annotated Training Data for Machine-Learning-Aided Biological Research Using Aerial Imagery." It comprises UAV aerial imagery used to execute the code available at https://github.com/pixelvar79/GAN-Flowering-Detection-paper. For detailed information on dataset usage and instructions for implementing the code to reproduce the study, please refer to the GitHub repository.
keywords: Plant phenotyping; generative and adversarial learning; phenotyping; UAV; UAS, drone
published: 2024-12-11
 
MMAudio pretrained models. These models can be used in the open-sourced codebase https://github.com/hkchengrex/MMAudio <b>Note:</b> mmaudio_large_44k_v2.pth and Readme.txt are added to this V2. Other 4 files stay the same.
planned publication date: 2025-05-01
 
BEPAM, Biofuel and Environmental Policy Analysis Model, models the agricultural sector and determines economically optimal land-use and feedstock mix at the US scale by maximizing the sum of agricultural sector consumers’ and producers’ surplus subject to various resource balances, land availability, and technological constraints under a range of biomass prices, from zero to $140 Mg-1 over the 2016-2030 period. Here BEPAM is used to model SAF production using energy crops and crop residues. BEPAM uses the GAMS format and uses yield and GHG balance projections from the biogeochemical model, DayCent.
keywords: BEPAM; Energy crops; direct and indirect land use change; soil carbon sequestration; fossil fuel displacement; economic incentives
published: 2024-12-05
 
This project investigates retraction indexing agreement among data sources: BCI, BIOABS, CCC, Compendex, Crossref, GEOBASE, MEDLINE, PubMed, Retraction Watch, Scopus, and Web of Science Core. Post-retraction citation may be partly due to authors’ and publishers' challenges in systematically identifying retracted publications. To investigate retraction indexing quality, we investigate the agreement in indexing retracted publications between 11 database sources, restricting to their coverage, resulting in a union list of 85,392 unique items. We also discuss common errors in indexing retracted publications. Our results reveal low retraction indexing agreement scores, indicating that databases widely disagree on indexing retracted publications they cover, leading to a lack of consistency in what publications are identified as retracted. Our findings highlight the need for clear and standard practices in the curation and management of retracted publications. Pipeline code to get the result files can be found in the GitHub repository https://github.com/infoqualitylab/retraction-indexing-agreement in the ‘src’ file containing iPython notebooks: The ‘unionlist_completed-ria_2024-07-09.csv’ file has been redacted to remove proprietary data, as noted below in README.txt. Among our sources, data is openly available only for Crossref, PubMed, and Retraction Watch. FILE FORMATS: 1) unionlist_completed-ria_2024-07-09.csv - UTF-8 CSV file 2) README.txt - text file
keywords: retraction status; data quality; indexing; retraction indexing; metadata; meta-science; RISRS
published: 2024-12-01
 
Healthy mares were kept at pasture for 3 weeks, stabled for 5 weeks, returned to pasture and an final sample collected 6 weeks later. Samples were collected weekly: gastric fluid by double-tube nasogastric intubation and aspiration, feces by rectal palpation. Microbial DNA was isolated using the QIAamp PowerFecal Pro DNA kit. Full length 16S, ITS and partial 23S rRNA gene libraries were created using the Shoreline Complete ID kit.
published: 2024-11-27
 
Honey bee (apis mellifera) MERFISH data set prepared by the Han lab, from brains collected by the Robinson lab at UIUC. Dataset is comprised of ~22 thousand cells and 130 genes with x,y locations for each cell. Jupyter notebook file is included as an example to load the data using Scanpy.
keywords: smFISH; single transcript spatial transcriptomics; Honey bee brain; Apis mellifera; MERFISH
published: 2020-09-02
 
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) https://doi.org/10.1016/S0012-3692(08)60339-6). <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
keywords: citation context annotation; retraction; diffusion of retraction; second-generation citation context analysis
published: 2024-10-18
 
Exhaustive species inventory of suburban wetland complex in northeast Ohio (Cuyahoga County).
keywords: floristic survey; wetland complex; comprehensive species list
published: 2024-10-16
 
School testing data were provided by Shield Illinois (ShieldIL), which conducted weekly in-school testing on behalf of the Illinois Department of Public Health (IDPH) for all participating schools in the state excluding Chicago Public Schools. The populations and proportions of students and employees in the studied school districts are reported by Elementary/Secondary Information System (ElSi) database.
keywords: COVID-19; school testing
published: 2023-07-05
 
The salt controversy is the public health debate about whether a population-level salt reduction is beneficial. This dataset covers 82 publications--14 systematic review reports (SRRs) and 68 primary study reports (PSRs)--addressing the effect of sodium intake on cerebrocardiovascular disease or mortality. These present a snapshot of the status of the salt controversy as of September 2014 according to previous work by epidemiologists: The reports and their opinion classification (for, against, and inconclusive) were from Trinquart et al. (2016) (Trinquart, L., Johns, D. M., & Galea, S. (2016). Why do we think we know what we know? A metaknowledge analysis of the salt controversy. International Journal of Epidemiology, 45(1), 251–260. https://doi.org/10.1093/ije/dyv184 ), which collected 68 PSRs, 14 SRRs, 11 clinical guideline reports, and 176 comments, letters, or narrative reviews. Note that our dataset covers only the 68 PSRs and 14 SRRs from Trinquart et al. 2016, not the other types of publications, and it adds additional information noted below. This dataset can be used to construct the inclusion network and the co-author network of the 14 SRRs and 68 PSRs. A PSR is "included" in an SRR if it is considered in the SRR's evidence synthesis. Each included PSR is cited in the SRR, but not all references cited in an SRR are included in the evidence synthesis or PSRs. Based on which PSRs are included in which SRRs, we can construct the inclusion network. The inclusion network is a bipartite network with two types of nodes: one type represents SRRs, and the other represents PSRs. In an inclusion network, if an SRR includes a PSR, there is a directed edge from the SRR to the PSR. The attribute file (report_list.csv) includes attributes of the 82 reports, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Notably, 11 PSRs have never been included in any SRR in the dataset. They are unused PSRs. If visualized with the inclusion network, they will appear as isolated nodes. We used a custom-made workflow (Fu, Y. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection ) that uses the Scopus API and manual work to extract and disambiguate authorship information for the 82 reports. The author information file (salt_cont_author.csv) is the product of this workflow and can be used to compute the co-author network of the 82 reports. We also provide several other files in this dataset. We collected inclusion criteria (the criteria that make a PSR eligible to be included in an SRR) and recorded them in the file systematic_review_inclusion_criteria.csv. We provide a file (potential_inclusion_link.csv) recording whether a given PSR had been published as of the search date of a given SRR, which makes the PSR potentially eligible for inclusion in the SRR. We also provide a bibliography of the 82 publications (supplementary_reference_list.pdf). Lastly, we discovered minor discrepancies between the inclusion relationships identified by Trinquart et al. (2016) and by us. Therefore, we prepared an additional edge list (inclusion_net_edges_trinquart.csv) to preserve the inclusion relationships identified by Trinquart et al. (2016). <b>UPDATES IN THIS VERSION COMPARED TO V2</b> (Fu, Yuanxi; Hsiao, Tzu-Kun; Joshi, Manasi Ballal (2022): The Salt Controversy Systematic Review Reports and Primary Study Reports Network Dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6128763_V2) - We added a new column "pub_date" to report_list.csv - We corrected mistakes in supplementary_reference_list.pdf for report #28 and report #80. The author of report #28 is not Salisbury D but Khaw, K.-T., & Barrett-Connor, E. Report #80 was mistakenly mixed up with report #81.
keywords: systematic reviews; evidence synthesis; network analysis; public health; salt controversy;