Dataset Search Results

Dataset providing calculation of a Competition Index (CI) for Late Pleistocene carnivore guilds in Laos and Vietnam and their relationship to humans. Prey mass spectra, Prey focus masses, and prey class raw data can be used to calculate the CI following Hemmer (2004). Mass estimates were calculated for each species following Van Valkenburgh (1990). Full citations to methodological papers are included as relationships with other resources

Contains scattering data obtained for (TaSe4)2I at the Advanced Photon Source at Argonne National Laboratory. Beamline 6ID-D was used with a beam energy of 64.8 keV in a transmission geometry. Data was obtained at temperatures between 28 and 300 K. See the readme.txt file for more information.

This dataset consists of a citation graph. It was constructed by downloading and parsing the Works section of the Open Alex catalog of the global research system. Open Alex (see citation below) contains detailed information about scholarly research, including articles, authors, journals, institutions, and their relationships. The data were downloaded on 2024-07-15. The dataset comprises two compressed (.xz) files. 1) filename: openalexID_integer_id_hasDOI.parquet.xz. The tabular data within contains three columns: openalex_id, integer_id, and hasDOI. Each row represents a record with the following data types: • openalex_id: A unique identifier from the Open Alex catalog. • integer_id: An integer representing the new identifier (assigned by the authors) • hasDOI: An integer (0 or 1) indicating whether the record has a DOI (0 for no, 1 for yes). 2) filename: citation_table.tsv.xz This edgelist of citations has two columns (no header) of integer values that represent citing and cited integer_id, respectively. Summary Features • Total Nodes (Documents): 256,997,006 • Total Edges (citations): 2,148,871,058 • Documents with DOIs: 163,495,446 • Edges between documents with DOIs: 1,936,722,541 The code used to generate these files can be found here: https://github.com/illinois-or-research-analytics/lorran_openalex/

Enclosed in this dataset are transport data of kagome connected artificial spin ice networks composed of permalloy nanowires. The data herein are reproductions of the data seen in Appendix B of the dissertation titled "Magnetotransport of Connected Artificial Spin Ice". Field sweeps with the magnetic field applied in-plane were performed in 5 degree increments for armchair orientation kagome artificial spin ice and zigzag orientation kagome artificial spin ice.

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} }

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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>.

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.

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.

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.

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]`

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.

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.

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.

School buses transport 20 million students annually and are currently undergoing electrification in the US. With Vehicle-to-Building (V2B) technology, electric school buses (ESBs) can supply energy to school buildings during power outages, ensuring continued operation and safety. This study proposes assessing the resilience of secondary schools during outages by leveraging ESB fleets as backup power across various US climate regions. The findings indicate that the current fleet of ESBs in representative cities across different climate regions in the US is insufficient to meet the power demands of an entire school or even its HVAC system. However, we estimated the number of ESBs required to support the school's power needs, and we showed that the use of V2B technology significantly reduces carbon emissions compared to backup diesel generators. While adjusting HVAC setpoints and installing solar panels have limited impacts on enhancing school resilience, gathering students in classrooms during outages significantly improved resilience in our case study in Houston, Texas. Given the ongoing electrification of school buses, it is essential for schools to complement ESBs with stationary batteries and other backup power sources, such as solar and/or diesel generators, to effectively address prolonged outages. Determining the deployment of direct current fast and Level 2 chargers can reduce infrastructure costs while maintaining the resilience benefits of ESBs. This dataset includes the simulation process and results of this study.

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