Dataset Search

Dataset associated with Jones et al. ECY22-0118.R3 submission: Ontogenetic effects of brood parasitism by the Brown-headed Cowbird on host offspring. Excel CSV files with all of the data used in analyses and file with descriptions of each column.

Trajectory data for Nature Nanotechnology manuscript "DNA double helix, a tiny electromotor" that demonstrates how an electric field applied along the helical axis of a DNA or RNA molecule will generate an electroosmotic flow that causes the duplex to spin about that axis, much like a turbine.

Prepared by Vetle Torvik 2021-05-07 The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters). • How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in December, 2018. (NLMs baseline 2018 plus updates throughout 2018). Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2018 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in: Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p • Look for Fig. 4 in the following article for coverage statistics over time: Palmblad, M., Torvik, V.I. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Trop Med Health 45, 33 (2017). <a href="https://doi.org/10.1186/s41182-017-0073-6">https://doi.org/10.1186/s41182-017-0073-6</a> Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014. • The code and back-end data is periodically updated and made available for query by PMID at http://abel.ischool.illinois.edu/cgi-bin/mapaffil/search.py • What is the format of the dataset? The dataset contains 52,931,957 rows (plus a header row). Each row (line) in the file has a unique PMID and author order, and contains the following eighteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1. 1. PMID: positive non-zero integer; int(10) unsigned 2. au_order: positive non-zero integer; smallint(4) 3. lastname: varchar(80) 4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed 5. initial_2: middle name initial 6. orcid: From 2019 ORCID Public Data File https://orcid.org/ and from PubMed XML 7. year: year of the publication 8. journal: name of journal that the publication is published 9. affiliation: author's affiliation?? 10. disciplines: extracted from departments, divisions, schools, laboratories, centers, etc. that occur on at least unique 100 affiliations across the dataset, some with standardization (e.g., 1770799), English translations (e.g., 2314876), or spelling corrections (e.g., 1291843) 11. grid: inferred using a high-recall technique focused on educational institutions (but, for experimental purposes, includes a few select hospitals, national institutes/centers, international companies, governmental agencies, and 200+ other IDs [RINGGOLD, Wikidata, ISNI, VIAF, http] for institutions not in GRID). Based on 2019 GRID version https://www.grid.ac/ 12. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK 13. city: varchar(200); typically 'city, state, country' but could include further subdivisions; unresolved ambiguities are concatenated by '|' 14. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA) 15. country 16. lat: at most 3 decimals (only available when city is not a country or state) 17. lon: at most 3 decimals (only available when city is not a country or state) 18. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find

This is the complete dataset for the "Anomalous density fluctuations in a strange metal" Proceedings of the National Academy of Sciences publication (https://doi.org/10.1073/pnas.1721495115). This is an integration of the Zenodo dataset which includes raw M-EELS data. <b>METHODOLOGICAL INFORMATION</b> 1. Description of methods used for collection/generation of data: Data have been collected with a M-EELS instrument and according to the data acquisition protocol described in the original PNAS publication and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026) 2. Methods for processing the data: Raw data were collected with a channeltron-based M-EELS apparatus described in the reference PNAS publication and analyzed according to the procedure outlined both in the PNAS paper and in SciPost Phys. 3, 026 (2017) (doi: 10.21468/SciPostPhys.3.4.026). The raw M-EELS spectra at each momentum have been subject to minor data processing involving: (a) averaging of different acquisitions at the same conditions, (b) energy binning, (c) division of an effective Coulomb matrix element (which yields a structure factor S(q,\omega)), (d) antisymmetrization (which yields the imaginary chi) All these procedures are described in the PNAS paper. 3. Instrument- or software-specific information needed to interpret the data: These data are simple .txt or .dat files which can be read with any standard data analysis software, notably Python notebooks, MatLab, Origin, IgorPro, and others. We do not include scripts in order to provide maximum flexibility. 4. Relationship between files, if important: We divided in different folders raw data, structure factors and imaginary chi. <b>DATA-SPECIFIC INFORMATION</b> There are 8 folders within the Data_public_deposition_v1.zip. Each folder contain data needed to create the corresponding figure in the publication. <b>1. Fig1:</b> This folder contains 21 DAT files needed to plot the theory data in panels C and D, following this naming conventions: [chiA]or[chiB]or[Pi]_q_number.dat With chiA is the imaginary RPA charge susceptibility with a Coulomb interaction of electronically weakly coupled layers chiB is the imaginary RPA charge susceptibility with the usual 4\pi e^2/q^2 Coulomb interaction. Pi is the imaginary Lindhard polarizability. q is momentum in reciprocal lattice units Number is the numerical momentum value in reciprocal lattice units <b>2. Fig2:</b> Files needed to plot Fig. 2 of the PNAS paper. Contains 3 folders as listed below. The files in this folder are named following this convention: Bi2212_295K_(1,-1)_50eV_161107_q_number_2.16_avg.dat, 295K is the sample temperature (1,-1) is the momentum direction in reciprocal lattice units 50 eV is the incident e beam energy 161107 is the start date of the experiment in yymmdd format Q is the momentum Number is the momentum in reciprocal lattice units 2.16 is the energy range covered by the data in eV Avg identifies averaged data ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>3. Fig3:</b> Files needed to plot Fig. 3 of the PNAS paper. OP/ OD prefix identifies optimally doped or overdosed sample data, respectively. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>4. Fig4:</b> Files needed to plot Fig. 4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>5. FigS1:</b> Files needed to plot Fig. S1 of the PNAS paper. There are 5 files in this folder. DAT files are M-EELS data following the prior naming convention, while the two .txt files are digitized data from N. Nücker, U. Eckern, J. Fink, and P. Müller, Long-Wavelength Collective Excitations of Charge Carriers in High-Tc Superconductors, Phys. Rev. B 44, 7155(R) (1991), and K. H. G. Schulte, The interplay of Spectroscopy and Correlated Materials, Ph.D. thesis, University of Groningen (2002). <b>6. FigS2:</b> Files needed to plot Fig. S2 of the PNAS paper. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra <b>7. FigS3:</b> Files needed to plot Fig. S3 of the PNAS paper. There are 2 files in this folder: 20K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 20 K 295K_phi_0_q_0.dat: is a M-EELS raw intensity at zero momentum transfer on Bi2212 at 295 K <b>8. FigS4:</b> Files needed to plot Fig. S4 of the PNAS paper. The _fit_parameters.dat file contains the fit parameters extracted according to the fit procedure described in the manuscript and at all momenta. ImChi: is the imaginary susceptibility obtained by antisymmetryzing the structure factor Raw_avg_data: raw averaged M-EELS spectra Sqw: Structure factors derived from the M-EELS spectra

This dataset includes the original data (including photographs as .jpg files and sound recordings as .wav files) and detailed descriptions of workflows for analyses of acoustic and morphometric data for the Neoaliturus tenellus (beet leafhopper) species complex. Files needed for different parts of the two analytical workflows are included in the "Acoustics.zip" and "PCA.zip" archives. The "Folder Structure.png" file contains a diagram of the folder structure of the two archives. Each archive contains a "ReadMe" file with instructions for repeating the analyses. File and folder names including the two-letter abbreviations TB, TD, TN and TP refer to four different putative species (operational taxonomic units, or OTUs, of the Neoaliturus tenellus complex.

Rhodosporidium toruloides is an oleaginous yeast capable of producing a variety of biofuels and bioproducts from diverse carbon sources. Despite numerous studies showing its promise as a platform microorganism, little is known about its metabolism and physiology. In this work, we investigated the central carbon metabolism in R. toruloides IFO0880 using transcriptomics and metabolomics during growth on glucose, xylose, acetate, or soybean oil. These substrates were chosen because they can be derived from plants. Significant changes in gene expression and metabolite concentrations were observed during growth on these four substrates. We mapped these changes onto the governing metabolic pathways to better understand how R. toruloides reprograms its metabolism to enable growth on these substrates. One notable finding concerns xylose metabolism, where poor expression of xylulokinase induces a bypass leading to arabitol production. Collectively, these results further our understanding of central carbon metabolism in R. toruloides during growth on different substrates. They may also help guide the metabolic engineering and development of better models of metabolism for R. toruloides.

The dataset includes the survey results about farmers’ perceptions of marginal land availability and the likelihood of a land pixel being marginal based on a machine learning model trained from the survey. Two spreadsheet files are the farmer and farm characteristics (marginal_land_survey_data_shared.xlsx), and the existing land use of marginal lands (land_use_info_sharing.xlsx). <b>Note:</b> the blank cells in these two spreadsheets mean missing values in the survey response. The GeoTiff file includes two bands, one the marginal land likelihood in the Midwestern states (0-1), the other the dominant reason of land marginality (0-5; 0 for farm size, 1 for growing season precipitation, 2 for root zone soil water capacity, 3 for average slope, 4 for growing season mean temperature, and 5 for growing season diurnal range of temperature). To read the data, please use a GIS software such as ArcGIS or QGIS.

This dataset contains soil chemical properties used to variation in soil fungal communities beneath Oreomunnea mexicana trees in the manuscript "Watershed-scale variation in potential fungal community contributions to ectomycorrhizal biogeochemical syndromes"

Efforts to engineer high-productivity crops to accumulate oils in their vegetative tissue present the possibility of expanding biodiesel production. However, processing the new crops for lipid recovery and ethanol production from cell wall saccharides is challenging and expensive. In a previous study using corn germ meal as a model substrate, we reported that liquid hot water (LHW) pretreatment enriched the lipid concentration by 2.2 to 4.2 fold. This study investigated combining oil recovery with ethanol production by extracting oil following LHW and simultaneous saccharification and co-fermentation (SSCF) of the biomass. Corn germ meal was again used to model the oil-bearing energy crops. Pretreated germ meal hydrolysate or solids (160 °C and 180 °C for 10 minutes) were fermented, and lipids were extracted from both the spent fermentation whole broth and fermentation solids, which were recovered by centrifugation and convective drying. Lipid contents in spent fermentation solids increased 3.7 to 5.7 fold compared to the beginning germ meal. The highest lipid yield achieved after fermentation was 36.0 mg lipid g−1 raw biomass; the maximum relative amount of triacylglycerol (TAG) was 50.9% of extracted oil. Although the fermentation step increased the lipid concentration of the recovered solids, it did not improve the lipid yields of pretreated biomass and detrimentally affected oil compositions by increasing the relative concentrations of free fatty acids.

The data provided in this submission are the gene annotations for the Illinois EBP pilot project samples, as well as the predicted proteins for each sample in FASTA format.

L-malic acid is widely used in the food, chemical, and pharmaceutical industries. Here, we report on production of malic acid from xylose, the second most abundant sugar in lignocellulosic hydrolysates, by engineered Saccharomyces cerevisiae. To enable malic acid production in a xylose-assimilating S. cerevisiae, we overexpressed PYC1 and PYC2, coding for pyruvate carboxylases, a truncated MDH3 coding for malate dehydrogenase, and SpMAE1, coding for a Schizosaccharomyces pombe malate transporter. Additionally, both the ethanol- and glycerol-producing pathways were blocked to enhance malic acid production. The resulting strain produced malic acid from both glucose and xylose, but it produced much higher titers of malic acid from xylose than glucose. Interestingly, the engineered strain had higher malic acid yield from lower concentrations (10 g L‒1) of xylose, with no ethanol production, than from higher xylose concentrations (20 and 40 g L‒1). As such, a fed-batch culture maintaining xylose concentrations at low levels was conducted and 61.2 g L‒1 of malic acid was produced, with a productivity of 0.32 g L‒1 h. These results represent successful engineering of S. cerevisiae for the production of malic acid from xylose, confirming that that xylose offers the efficient production of various biofuels and chemicals by engineered S. cerevisiae.

Supplemental document corresponding to a submission to Physiological Genomics (Data supplements and source materials must now be deposited in a community-recognized data repository or to a generalist public access repository if no community resource is available. See "Author/Production Requirements" for more information.) https://pg.msubmit.net/

This dataset contains white-tailed deer (Odocoileus virginianus) land cover utility score (deer LCU score) data for every TRS (township, range, and section), township-range, and county in Illinois, USA, based on annual National Land Cover Database (NLCD) data released for all years between 2000 and 2023. LCU data is provided in CSV files for each spatial scale, with TRS data split into 2 CSV files due to size limits. Rasters (TIF) showing all deer habitat in Illinois are also provided to show the location, quality, and quantity of deer habitat. A metadata file is also included for additional information.

This repository contains the weights for two StyleGAN2 networks trained on two composite T1 and T2 weighted open-source brain MR image datasets, and one StyleGAN2 network trained on the Flickr Face HQ image dataset. Example images sampled from the respective StyleGANs are also included. The datasets themselves are not included in this repository. The weights are stored as `.pkl` files. The code and instructions to load and use the weights can be found at https://github.com/comp-imaging-sci/pic-recon . Additional details and citations can be found in the file "README.md".

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

Root exudation is a key process for plant nutrient acquisition, but the controls on root exudation and its relationship to soil C and N processes in agroecosystems are unclear. We hypothesized that root exudation rates would be related to root morphological traits, N fertilization, and soil moisture. We also anticipated that root exudation would be correlated with bulk soil enzyme activity. Root exudation, root traits, and bulk soil extracellular enzyme activity were assessed in maize (Zea mays L.), soybean (Glycine max (L.) Merr.), biomass sorghum (Sorghum bicolor (L.) Moench), giant miscanthus (Miscanthus × giganteus), and switchgrass (Panicum virgatum L.). Measurements were taken in situ during two growing seasons with contrasting precipitation regimes, and N fertilization rate was varied in sorghum during one year. Specific root exudation (per unit root surface area) was negatively related to root diameter and was generally higher in annuals than perennials. Sorghum N fertilization did not affect root exudation rates, and soil moisture regime had no effect on annual root exudation rates within maize, sorghum, and miscanthus. Specific root exudation was negatively related to bulk soil C- and N-degrading soil enzyme activities. Intrinsic plant characteristics appeared more important than environmental variables in controlling in situ root exudation rates. The relationships between root diameter, root exudation, and soil C and N processes link root morphological traits to soil functions and demonstrate the potential tradeoffs among plant nutrient acquisition strategies in agroecosystems.

The data files are for the paper entitled: Long-lifetime spin excitations near domain walls in 1T-TaS2 to be published in PNAS. The data was obtained on a 300 mK custom designed Unisoku scanning tunneling microscope using the Nanonis module. All the data files have been named based on the Figure numbers that they represent.

This dataset contains model code (including input data) to replicate the outcomes for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States". The model consists of: (1) The replication codes and data for the model. To run the model, using GAMS to run the "Models.gms" file.

The availability of economically marginal land for energy crops is identified using the Cropland Data Layer and other soil, wind, climate data resources. All data are recognized on a 30m spatial resolution across the continental United States.

This dataset is a snapshot of the presence and structure of entrepreneurship education in U.S. four-year colleges and universities in 2015, including co-curricular activities and related infrastructure. Public, private not-for-profit and for-profit institutions are included, as are specialized four-year institutions. The dataset provides insight into the presence of entrepreneurship education both within business units and in other units of college campuses. Entrepreneurship is defined broadly, to include small business management and related career-focused options.

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

To gather news articles from the web that discuss the Cochrane Review (DOI: 10.1002/14651858.CD006207.pub6), we retrieved articles on August 1, 2023 from used Altmetric.com's Altmetric Explorer. We selected all articles that were written in English, published in the United States, and had a publication date <b>on or after March 10, 2023</b> (according to the "Mention Date" from Altmetric.com). This date is significant as it is when Cochrane issued a statement (https://www.cochrane.org/news/statement-physical-interventions-interrupt-or-reduce-spread-respiratory-viruses-review) about the "misleading interpretation" of the Cochrane Review made by news articles. A previously published dataset for "Arguing about Controversial Science in the News: Does Epistemic Uncertainty Contribute to Information Disorder?" (DOI: 10.13012/B2IDB-4781172_V1) contains annotation of the news articles published before March 10, 2023. Our dataset annotates the news published on or after March 10, 2023. The Altmetric_data.csv describes the selected news articles with both data exported from Altmetric Explorer and data we manually added Data exported from Altmetric Explorer: - Publication date of the news article - Title of the news article - Source/publication venue of the news article - URL - Country Data we manually added: - Whether the article is accessible - The date we checked the article - The corresponding ID of the article in MAXQDA For each article from Altmetric.com, we first tried to use the Web Collector for MAXQDA to download the article from the website and imported it into MAXQDA (version 22.8.0). We manually extracted direct quotations from the articles using MAXQDA. We included surrounding words and sentences around direct quotations for context where needed. We manually added codes and code categories in MAXQDA to identify the individuals (chief editors of the Cochrane Review, government agency representatives, journalists, and other experts such as physicians) or organizations (government agencies, other organizations, and research publications) who were quoted. The MAXQDA_data.csv file contains excerpts from the news articles that contain the direct quotations we annotated. For each excerpt, we included the following information: - MAXQDA ID of the document from which the excerpt originates - The collection date and source of the document - The code we assigned to the excerpt - The code category - The excerpt itself

β‐Carotene is a natural pigment and health‐promoting metabolite, and has been widely used in the nutraceutical, feed, and cosmetic industries. Here, we engineered a GRAS yeast Saccharomyces cerevisiae to produce β‐carotene from xylose, the second most abundant and inedible sugar component of lignocellulose biomass. Specifically, a β‐carotene biosynthetic pathway containing crtYB, crtI, and crtE from Xanthophyllomyces dendrorhous was introduced into a xylose‐fermenting S. cerevisiae. The resulting strain produced β‐carotene from xylose at a titer threefold higher than from glucose. Interestingly, overexpression of tHMG1, which has been reported as a critical genetic perturbation to enhance metabolic fluxes in the mevalonate pathway and β‐carotene production in yeast when glucose is used, did not further improve the production of β‐carotene from xylose. Through fermentation profiling, metabolites analysis, and transcriptional studies, we found the advantages of using xylose as a carbon source, instead of glucose, for β‐carotene production to be a more respiratory feature of xylose consumption, a larger cytosolic acetyl‐CoA pool, and an upregulated expression level of rate‐limiting genes in the β‐carotene‐producing pathway, including ACS1 and HMG1. As a result, 772.8 mg/L of β‐carotene was obtained in a fed‐batch bioreactor culture with xylose feeding. Considering the inevitable large scale production of xylose when cellulosic biomass‐based bioeconomy is implemented, our results suggest xylose utilization is a promising strategy for overproduction of carotenoids and other isoprenoids in engineered S. cerevisiae.

Sugarcane is being enhanced as a bioenergy crop by engineering it to accumulate and store lipids along with polymeric sugars in vegetative tissues. However, there is no existing process that allows for processing this new crop to recover both lipid and cellulosic sugars from the oilcane bagasse. Therefore, a comprehensive investigation of two pretreatment methods—natural deep eutectic solvents (NADES) and chemical-free hydrothermal pretreatment (HT) was conducted to judge their suitability for recovering fermentable sugars, lipids, and lignin from bagasse. Two NADES, i.e., choline chloride: lactic acid (ChCl:LA) and betaine: lactic acid (BT:LA) were prepared using a 1:2 M ratio and were evaluated for pretreatment of oilcane bagasse at 10, 20, and 50 % (w/w) solids, followed by enzymatic hydrolysis at 10 % (w/w) solids. Notably, ChCl:LA NADES treatment at 10 % (w/w) solids at 140 °C for 2 h, solubilized 78.8 % of lignin and 80.4 % of hemicellulose and allowed 82.7 % enzymatic conversion of glucans to glucose. In contrast, HT pretreatment removed approximately 87.6 % of the hemicellulose and provided an enzymatic glucose yield of 69.7 %. Furthermore, ChCl:LA operated at 50 % solids loading the enriched lipids 2.6-fold (9.2 wt%) in recovered solids compared to HT (6.4 %) and BT:LA (5.1 %) pretreatment processes. NMR-HSQC and GPC analysis showed that ChCl:LA also cleaved the most lignin β–O–4 linkages and demonstrated lower molecular weight compared to HT. This study demonstrates that NADES pretreatment is an effective green processing method for recovering lipids, sugars, and lignin from bioenergy crops at high solid loading (50 % w/w) within the context of an integrated biorefinery.

An online knowledge, attitudes, and practices survey on ticks and tick-borne diseases was distributed to medical professionals in Illinois during summer 2020 to fall 2021. These are the raw data associated with that survey and the survey questions used. Age, gender, and county of practice have been removed for identifiability. We have added calculated values (columns 165 to end), including: the tick knowledge score, TBD knowledge score, and total knowledge score, which are the sum of the total number of correct answers in each category, and score percent, which are the proportion of correct answers in each category; region, which is determined from the county of practice; TBD relevant practice, which separates the practice variable into TBD primary, secondary, and non-responders; and several variables which group categories.