Dataset Search Results

This repository contains the data and computational analysis notebooks that were used in the following manuscript. For more information on the methods and contributing authors, please refer to the original manuscript. "Beyond A and B Compartments: how major nuclear locales define nuclear genome organization and function Omid Gholamalamdari et al. 2024"

The immunofluorescence and segmented images of three nuclear locales, (nuclear periphery, nuclear speckles, and nucleolus) in four human cells lines (H1-hESC, HCT116, HFFc6, and K562). For each of the cell lines, this dataset includes original, cropped, and binary 4D images (3D + antibody) in addition to max projected thumbnails of cell nuclei.

This dataset is derived from the COCI, the OpenCitations Index of Crossref open DOI-to-DOI references (opencitations.net). Silvio Peroni, David Shotton (2020). OpenCitations, an infrastructure organization for open scholarship. Quantitative Science Studies, 1(1): 428-444. https://doi.org/10.1162/qss_a_00023 We have curated it to remove duplicates, self-loops, and parallel edges. These data were copied from the Open Citations website on May 6, 2023 and subsequently processed to produce a node list and an edge-list. Integer_ids have been assigned to the DOIs to reduce memory and storage needs when working with these data. As noted on the Open Citation website, each record is a citing-cited pair that uses DOIs as persistent identifiers.

Zika virus (ZIKV) infection has been linked to neurological disorders such as microcephaly in children. Cases of Guillain-Barré Syndrome (GBS), a peripheral nervous system (PNS) disorder, have been reported in adults with ZIKV infection. These ZIKV-related GBS cases often exhibit atypical clinical features compared to classic GBS, including central nervous system (CNS) involvement. This dataset comprises two patient groups and a healthy control group. The first patient group includes adults with confirmed ZIKV infection, presenting both PNS-related GBS symptoms and CNS manifestations. The second group consists of adults with GBS but without ZIKV infection. The final group includes healthy, unaffected individuals.

The Cline Center Historical Phoenix Event Data covers the period 1945-2019 and includes 8.2 million events extracted from 21.2 million news stories. This data was produced using the state-of-the-art PETRARCH-2 software to analyze content from the New York Times (1945-2018), the BBC Monitoring's Summary of World Broadcasts (1979-2019), the Wall Street Journal (1945-2005), and the Central Intelligence Agency’s Foreign Broadcast Information Service (1995-2004). It documents the agents, locations, and issues at stake in a wide variety of conflict, cooperation and communicative events in the Conflict and Mediation Event Observations (CAMEO) ontology. The Cline Center produced these data with the generous support of Linowes Fellow and Faculty Affiliate Prof. Dov Cohen and help from our academic and private sector collaborators in the Open Event Data Alliance (OEDA). For details on the CAMEO framework, see: Schrodt, Philip A., Omür Yilmaz, Deborah J. Gerner, and Dennis Hermreck. "The CAMEO (conflict and mediation event observations) actor coding framework." In 2008 Annual Meeting of the International Studies Association. 2008. http://eventdata.parusanalytics.com/papers.dir/APSA.2005.pdf Gerner, D.J., Schrodt, P.A. and Yilmaz, O., 2012. Conflict and mediation event observations (CAMEO) Codebook. http://eventdata.parusanalytics.com/cameo.dir/CAMEO.Ethnic.Groups.zip For more information about PETRARCH and OEDA, see: http://openeventdata.org/

These are the datasets underlying the figures in the manuscript "Methods of active surveillance for hard ticks and associated tick-borne pathogens of public health importance in the contiguous United States: A Comprehensive Systematic Review". The review considered only publications reporting on active tick or tick-borne pathogen surveillance in the contiguous United States published between 1944 and 2018. For the purposes of this review, we were only concerned with studies of Ixodidae (hard ticks) and/or studies of tick-borne pathogens (in humans, animals, or hard ticks) of public health importance to humans. Study designs included cross-sectional, serological, epidemiological, ecological, or observational studies. Only peer-reviewed publications published in the English language were included. Studies were excluded if they focused on a tick that is not a vector of a human pathogen or on a pathogen that does not cause disease in humans, if the tick or tick-borne pathogen findings were incidental, or if they did not include quantitative surveillance data. For the purpose of this study, we defined surveillance data as information on ticks or pathogens provided through active sampling in natural areas; it should be noted that this does not match the strict definition used by the CDC, which requires sustained sampling efforts across time. Studies were also excluded if they: explored regions other than the contiguous US; focused on treatment, vaccine, or therapeutics development and/or diagnostics of human disease; focused on tick or pathogen genetics; focused on experimental studies with ticks or hosts; were tick control and/or management studies; performed only passive surveillance; were review articles; were not peer reviewed; were in a language other than English; the full text was not available; and if the disease was not a risk to the general public. In addition, for articles which reported data that had previously been published, we only included previously unreported information collected by the authors, and we referenced the specific period of collection for these data to ensure we were not double-recording data. Due to publication delays, we also performed a non-systematic review of the literature of articles published between 2019 – 2023 on tick and tickborne pathogen surveillance methods conducted in the contiguous United States. Keyword search was performed in PubMed Central and Web of Science Core Collection databases. The search algorithm keywords included tick(s), Amblyomma, Dermacentor, Ixodes, Rhipicephalus, Acari Ixodidea, tick host(s), Lyme disease, Rocky Mountain Spotted Fever, Spotted Fever Group, Rickettsiosis, Ehrlichiosis, Anaplasmosis, Borreliosis, Tularemia, Babesiosis, tick-borne pathogen, Powassan, Heartland, Bourbon, Colorado tick fever, Pacific Coast tick fever, tick surveillance, surveillance, (sero)epidemiology, prevalence, distribution, ecology, United States. The search algorithm utilized is provided as follows: TI= ((ticks OR Ixodes OR Amblyomma OR Dermacentor OR Rhipicephalus OR "Acari Ixodidi" OR "tick hosts" OR "tick host") OR ("Lyme Disease" OR "Rocky Mountain Spotted Fever" OR "Spotted Fever Group" OR Rickettsiosis OR Rickettsial OR Ehrlichiosis OR Anaplasmosis OR Borreliosis OR Tularemia OR Babesiosis OR Borrelia OR Ehrlichia OR Anaplasma OR Rickettsia OR Babesia OR "tick-borne pathogen" OR "tick borne pathogen")) AND TS= ("tick surveillance" OR surveillance OR epidemiology OR seroepidemiology OR ecology) AND CU=("United States of America" OR "USA" OR "United States" OR United-States). These datasets are the collated data underlying the figures in the manuscript. For more details, please see the publication. The following are explanations for variables used in all the CSV files: Tick: Species of tick collected Tick_Method: Method of collecting ticks Pathogen: Species of pathogen tested for Path_Method: Method of testing for pathogens Decade: Decade of publication n: Number of publications STATE: state in which study was conducted COUNTY: county in which study was conducted 1944 - 2018 (Was surveillance performed?): was there at least one publication included with a publication date within the 1944-2018 period in this geographic region? 2019 - 2023 (Was surveillance performed?): was there at least one publication included with a publication date within the 2019-2023 period in this geographic region?

#Overview These are reference packages for the TIPP3 software for abundance profiling and/or species detection from metagenomic reads (e.g., Illumina, PacBio, Nanopore, etc.). Different refpkg versions are listed. TIPP3 software: https://github.com/c5shen/TIPP3 #Changelog V1.2 (`tipp3-refpkg-1-2.zip`) >>Fixed old typos in the file mapping text. >>Added new files `taxonomy/species_to_marker.tsv` for new function `run_tipp3.py detection [...parameters]`. Please use the latest release of the TIPP3 software for this new function. V1 (`tipp3-refpkg.zip`) >>Initial release of the TIPP3 reference package. #Usage 1. unzip the file to a local directory (will get a folder named "tipp3-refpkg"). 2. use with TIPP3 software: `run_tipp3.py -r [path/to/tipp3-refpkg] [other parameters]`

Rising global temperatures and urban heat island effects challenge environmental health and energy systems at the city level, particularly in summer. Increased heatwaves raise energy demand for cooling, stressing power facilities, increasing costs, and risking blackouts. Heat impacts vary across cities due to differences in urban morphology, geography, land use, and land cover, highlighting vulnerable areas needing targeted heat mitigation. Urban tree canopies, a nature-based solution, effectively mitigate heat. Trees provide shade and cooling through evaporation, improving thermal comfort, reducing air conditioning energy consumption, and enhancing climate resilience. This report focused on the ComEd service area in the Chicago Metropolitan Region and assessed the impacts of population growth, urbanization, climate change, and an ambitious plan to plant 1 million trees. The report evaluated planting 1 million trees to quantify regional cooling effects projected for the 2030s. Afforestation locations were selected to avoid interference with existing infrastructure. Key findings include (i) extreme hot hours (>95°F) will increase from 30 to 200 per year, adding 420 Cooling Degree Days (CCD) by the 2030s, (ii) greener areas can be up to 10°F cooler than less vegetated neighborhoods in summer, (iii) tree canopies can create localized cooling, reducing temperatures by 0.7°F and lowering annual CCD by 60 to 65, and (iv) afforestation can reduce the region’s temperature by 0.7°F, saving 400 to 1100 Megawatt hours of daily power usage during summer. <b>Note: The data is available upon request from <a href="mailto:dpiclimate@uilliois.edu">dpiclimate@uilliois.edu.

Data for the invertebrate analysis in chapter 2 of Jacob Ridgway's thesis: "Neonicotinoids and Fungicides Alter Soil Invertebrate Abundance and Richness Within Restored Prairie"

This dataset, uCite, is the union of nine large-scale open-access PubMed citation data separated by reliability. There are 20 files, including the reliable and unreliable citation PMID pairs, non-PMID identifiers to PMID mapping (for DOIs, Lens, MAG, and Semantic Scholar), original PMID pairs from the nine resources, some metadata for PMIDs, duplicate PMIDs, some redirected PMID pairs, and PMC OA Patci citation matching results. The short description of each data file is listed as follows. A detailed description can be found in the README.txt. <strong>DATASET DESCRIPTION</strong> <ol> <li>PPUB.tsv.gz - tsv format file containing reliable citation pairs uCite.</li> <li>PUNR.tsv.gz - tsv format file containing reliable citation pairs uCite.</li> <li>DOI2PMID.tsv.gz - tsv format file containing results mapping DOI to PMID. </li> <li> LEN2PMID.tsv.gz - tsv format file containing results mapping LensID pairs to PMID pairs.. </li> <li> MAG2PMIDsorted.tsv.gz - tsv format file containing results mapping MAG ID to PMID. </li> <li>SEM2PMID.tsv.gz - tsv ormat file containing results mapping Semantic Scholar ID to PMID. </li> <li>JVNPYA.tsv.gz - tsv format file containing metadata of papers with PMID, journal name, volume, issue, pages, publication year, and first author's last name. </li> <li>TiLTyAlJVNY.tsv.gz - tsv format file containing metadata of papers. </li> <li> PMC-OA-patci.tsv.gz - tsv format file containing PubMed Central Open Access subset reference strings extracted by \cite{} processed by Patci.</li> <li>REDIRECTS.gz - txt file containing unreliable PMID pairs mapped to reliable PMID pairs. </li> <li>REMAP - file containing pairs of duplicate PubMed records (lhs PMID mapped to rhs PMID).</li> <li> ami_pair.tsv.gz - tsv format file containing all citation pairs from Aminer (2015 version). </li> <li> dim_pair.tsv.gz - tsv format file containing all citation pairs from Dimensions. </li> <li> ice_pair.tsv.gz - tsv format file containing all citation pairs from iCite (April 2019 version, version 1). </li> <li> len_pair.tsv.gz - tsv format file containing all citation pairs from Lens.org (harvested through Oct 2021). </li> <li>mag_pair.tsv.gz - tsv format file containing all citation pairs from Microsoft Academic Graph (2015 version). </li> <li> oci_pair.tsv.gz - tsv format file containing all citation pairs from Open Citations (Nov. 2021 dump, csv version ). </li> <li> pat_pair.tsv.gz - tsv format file containing all citation pairs from Patci (i.e., from "PMC-OA-patci.tsv.gz"). </li> <li> pmc_pair.tsv.gz - tsv format file containing all citation pairs from PubMed Central (harvest through Dec 2018 via e-Utilities).</li> <li> sem_pair.tsv.gz - tsv format file containing all citation pairs from Semantic Scholar (2019 version) . </li> </ol> <strong>COLUMN DESCRIPTION</strong> <strong>FILENAME</strong> : <em>PPUB.tsv.gz, PUNR.tsv.gz</em> (1) fromPMID - PubMed ID of the citing paper. (2) toPMID - PubMed ID of the cited paper. (3) sources - citation sources, in which the citation pairs are identified. (4) fromYEAR - Publication year of the citing paper. (5) toYEAR - Publication year of the cited paper. <strong>FILENAME</strong> : <em>DOI2PMID.tsv.gz</em> (1) DOI - Semantic Scholar ID of paper records. (2) PMID - PubMed ID of paper records. (3) PMID2 - Digital Object Identifier of paper records, “-” if the paper doesn't have DOIs. <strong>FILENAME</strong> : <em>SEMID2PMID.tsv.gz</em> (1) SemID - Semantic Scholar ID of paper records. (2) PMID - PubMed ID of paper records. (3) DOI - Digital Object Identifier of paper records, “-” if the paper doesn't have DOIs. <strong>FILENAME</strong> : <em>JVNPYA.tsv.gz</em> - Each row refers to a publication record. (1) PMID - PubMed ID. (2) journal - Journal name. (3) volume - Journal volume. (4) issue - Journal issue. (5) pages - The first page and last page (without leading digits) number of the publication separated by '-'. (6) year - Publication year. (7) lastname - Last name of the first author. <strong>FILENAME</strong> : <em>TiLTyAlJVNY.tsv.gz</em> (1) PMID - PubMed ID. (2) title_tokenized - Paper title after tokenization. (3) languages - Language that paper is written in. (4) pub_types - Types of the publication. (5) length(authors) - String length of author names. (6) journal -Journal name . (7) volume - Journal volume . (8) issue - Journal issue. (9) year - Publication year of print (not necessary epub). <strong>FILENAME</strong> : <em> PMC-OA-patci.tsv.gz</em> (1) pmcid - PubMed Central identifier. (2) pos - (3) fromPMID - PubMed ID of the citing paper. (4) toPMID - PubMed ID of the cited paper. (5) SRC - citation sources, in which the citation pairs are identified. (6) MatchDB - PubMed, ADS, DBLP. (7) Probability - Matching probability predicted by Patci. (8) toPMID2 - PubMed ID of the cited paper, extracted from OA xml file (9) SRC2 - citation sources, in which the citation pairs are identified. (10) intxt_id - (11) jounal - First character of the journal name. (12) same_ref_string - Y if patci and xml reference string match, otherwise N. (13) DIFF - (14) bestSRC - Citation sources, in which the citation pairs are identified. (15) Match - Matching strings annotated by Patci. <strong>FILENAME</strong> : <em>REDIRECTS.gz</em> Each row in Redirectis.txt is a string sequence in the same format as follows. - "REDIRECTED FROM: source PMID_i PMID_j -> PMID_i' PMID_j " - "REDIRECTED TO: source PMID_i PMID_j -> PMID_i PMID_j' " Note: source is the names of sources where the PMID_i and PMID_j are from. <strong>FILENAME</strong> : <em>REMAP</em> Each row is remapping unreliable PMID pairs mapped to reliable PMID pairs. The format of each row is "$REMAP{PMID_i} = PMID_j". <strong>FILENAME</strong> : <em>ami_pair.tsv.gz, dim_pair.tsv.gz, ice_pair.tsv.gz, len_pair.tsv.gz, mag_pair.tsv.gz, oci_pair.tsv.gz, pat_pair.tsv.gz，pmc_pair.tsv.gz, sem_pair.tsv.gz</em> (1) fromPMID - PubMed ID of the citing paper. (2) toPMID - PubMed ID of the cited paper.

This data set includes information on mixing metric values and distances to determine the average length scale, rates and variability of mixing downstream of 43 river confluences for 150 mixing events. The file "pmx_all data.csv" contains confluence names, the number of events per confluence site, and Pmx values measured at various actual and dimensionless downstream distances. The file "pmx_binned data.csv" provides mean Pmx values within 0.5-unit dimensionless distance bins.

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.

ICoastalDB, which was developed using Microsoft structured query language (SQL) Server, consists of water quality and related data in the Illinois coastal zone that were collected by various organizations. The information in the dataset includes, but is not limited to, sample data type, method of data sampling, location, time and date of sampling and data units.

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.

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the <a href="https://docs.google.com/forms/d/e/1FAIpQLSf-J937V6I4sMSxQt7gR3SIbUASR26KXxqSurrkBvlF-CIQnQ/viewform?usp=pp_url"><b>Archer Access Request Form</b></a> so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the <a href="https://forms.gle/6eA2yJUGFMtj5swY7"><b>Archer User Feedback Form</b></a>. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this <a href="https://groups.webservices.illinois.edu/subscribe/154221"><b>form</b></a> to subscribe to it. <b>Citation Guidelines:</b> 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2025. Global News Index and Extracted Features Repository [codebook], v1.3.0. Champaign, IL: University of Illinois. June. XX. doi:10.13012/B2IDB-5649852_V6 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2025. Global News Index and Extracted Features Repository [database], v1.3.0. Champaign, IL: University of Illinois. Jun. XX. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V6 *NOTE: V6 is replacing V5 with updated ‘Archer’ documents to reflect changes made to the Archer system.

Hype - PubMed dataset Prepared by Apratim Mishra This dataset captures ‘Hype’ within biomedical abstracts sourced from PubMed. The selection chosen is ‘journal articles’ written in English, published between 1975 and 2019, totaling ~5.2 million. The classification relies on the presence of specific candidate ‘hype words’ and their abstract location. Therefore, each article (PMID) might have multiple instances in the dataset due to the presence of multiple hype words in different abstract sentences. The candidate hype words are 35 in count: 'major', 'novel', 'central', 'critical', 'essential', 'strongly', 'unique', 'promising', 'markedly', 'excellent', 'crucial', 'robust', 'importantly', 'prominent', 'dramatically', 'favorable', 'vital', 'surprisingly', 'remarkably', 'remarkable', 'definitive', 'pivotal', 'innovative', 'supportive', 'encouraging', 'unprecedented', 'enormous', 'exceptional', 'outstanding', 'noteworthy', 'creative', 'assuring', 'reassuring', 'spectacular', and 'hopeful’. This is version 3 of the dataset. Added new file - WSD_hype.tsv File 1: hype_dataset_final.tsv Primary dataset. It has the following columns: 1. PMID: represents unique article ID in PubMed 2. Year: Year of publication 3. Hype_word: Candidate hype word, such as ‘novel.’ 4. Sentence: Sentence in abstract containing the hype word. 5. Hype_percentile: Abstract relative position of hype word. 6. Hype_value: Propensity of hype based on the hype word, the sentence, and the abstract location. 7. Introduction: The ‘I’ component of the hype word based on IMRaD 8. Methods: The ‘M’ component of the hype word based on IMRaD 9. Results: The ‘R’ component of the hype word based on IMRaD 10. Discussion: The ‘D’ component of the hype word based on IMRaD File 2: hype_removed_phrases_final.tsv Secondary dataset with same columns as File 1. Hype in the primary dataset is based on excluding certain phrases that are rarely hype. The phrases that were removed are included in File 2 and modeled separately. Removed phrases: 1. Major: histocompatibility, component, protein, metabolite, complex, surgery 2. Novel: assay, mutation, antagonist, inhibitor, algorithm, technique, series, method, hybrid 3. Central: catheters, system, design, composite, catheter, pressure, thickness, compartment 4. Critical: compartment, micelle, temperature, incident, solution, ischemia, concentration, thinking, nurses, skills, analysis, review, appraisal, evaluation, values 5. Essential: medium, features, properties, opportunities, oil 6. Unique: model, amino 7. Robust: regression 8. Vital: capacity, signs, organs, status, structures, staining, rates, cells, information 9. Outstanding: questions, issues, question, questions, challenge, problems, problem, remains 10. Remarkable: properties 11. Definite: radiotherapy, surgery File 3: WSD_hype.tsv Includes hype-based disambiguation for candidate words targeted for WSD (Word sense disambiguation)

References - Li, Fu, Umberto Villa, Seonyeong Park, and Mark A. Anastasio. "3-D stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69, no. 1 (2021): 135-146. DOI: 10.1109/TUFFC.2021.3112544 - Li, Fu; Villa, Umberto; Park, Seonyeong; Anastasio, Mark, 2021, "2D Acoustic Numerical Breast Phantoms and USCT Measurement Data", https://doi.org/10.7910/DVN/CUFVKE, Harvard Dataverse, V1 Overview - This dataset includes 1,089 two-dimensional slices extracted from 3D numerical breast phantoms (NBPs) for ultrasound computed tomography (USCT) studies. The anatomical structures of these NBPs were obtained using tools from the Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) project. The methods used to modify and extend the VICTRE NBPs for use in USCT studies are described in the publication cited above. - The NBPs in this dataset represent the following four ACR BI-RADS breast composition categories: > Type A - The breast is almost entirely fatty > Type B - There are scattered areas of fibroglandular density in the breast > Type C - The breast is heterogeneously dense > Type D - The breast is extremely dense - Each 2D slice is taken from a different 3D NBP, ensuring that no more than one slice comes from any single phantom. File Name Format - Each data file is stored as an HDF5 .mat file. The filenames follow this format: {type}{subject_id}.mat where{type} indicates the breast type (A, B, C, or D), and {subject_id} is a unique identifier assigned to each sample. For example, in the filename D510022534.mat, "D" represents the breast type, and "510022534" is the sample ID. File Contents - Each file contains the following variables: > "type": Breast type > "sos": Speed-of-sound map [mm/μs] > "den": Ambient density map [kg/mm³] > "att": Acoustic attenuation (power-law prefactor) map [dB/ MHzʸ mm] > "y": power-law exponent > "label": Tissue label map. Tissue types are denoted using the following labels: water (0), fat (1), skin (2), glandular tissue (29), ligament (88), lesion (200). - All spatial maps ("sos", "den", "att", and "label") have the same spatial dimensions of 2560 x 2560 pixels, with a pixel size of 0.1 mm x 0.1 mm. - "sos", "den", and "att" are float32 arrays, and "label" is an 8-bit unsigned integer array.

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

This dataset contains raw data of plasma glucose, insulin, c-peptide, GLP-1, and FGF21 collected as part of a study aimed to study alcohol pharmacokinetics in women who underwent metabolic surgery.

To gather news articles from the web that discuss the Cochrane Review, we used Altmetric Explorer from Altmetric.com and retrieved articles on August 1, 2023. We selected all articles that were written in English, published in the United States, and had a publication date <b>prior to March 10, 2023</b> (according to the “Mention Date” on Altmetric.com). This date is significant as it is when Cochrane issued a statement about the "misleading interpretation" of the Cochrane Review. The collection of news articles is presented in the Altmetric_data.csv file. The dataset contains the following data that we exported from Altmetric Explorer: - Publication date of the news article - Title of the news article - Source/publication venue of the news article - URL - Country We manually checked and added the following information: - Whether the article still exists - Whether the article is accessible - Whether the article is from the original source We assigned MAXQDA IDs to the news articles. News articles were assigned the same ID when they were (a) identical or (b) in the case of Article 207, closely paraphrased, paragraph by paragraph. Inaccessible items were assigned a MAXQDA ID based on their "Mention Title". 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 22.7.0). If an article could not be retrieved using the Web Collector, we either downloaded the .html file or in the case of Article 128, retrieved it from the NewsBank database through the University of Illinois Library. We then manually extracted direct quotations from the articles using MAXQDA. We included surrounding words and sentences, and in one case, a news agency’s commentary, around direct quotations for context where needed. The quotations (with context) are the positions in our analysis. We also identified who was quoted. We excluded quotations when we could not identify who or what was being quoted. We annotated quotations with codes representing groups (government agencies, other organizations, and research publications) and individuals (authors of the Cochrane Review, government agency representatives, journalists, and other experts such as epidemiologists). The MAXQDA_data.csv file contains excerpts from the news articles that contain the direct quotations we identified. 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 with which the excerpt is annotated; - The code category; - The excerpt itself.

The files are plain text and contain the original data used in phylogenetic analyses of of Typhlocybinae (Bin, Dietrich, Yu, Meng, Dai and Yang 2022: Ecology & Evolution, in press). The three files with extension .phy are text files with aligned DNA sequences in the standard PHYLIP format and correspond to Matrix 1 (amino acid alignment), Matrix 2 (nucleotide alignment of first two codon positions of protein-coding genes) and Matrix 3 (nucleotide alignment of protein-coding genes plus 2 ribosomal genes) described in the Methods section. An additional text file in NEXUS format (.nex extension) contains the morphological character data used in the ancestral state reconstruction (ASCR) analysis described in the Methods. NEXUS is a standard format used by various phylogenetic analysis software. For more information on data file content, see the included "readme" files.

The Membracoidea_morph_data_Final.nex text file contains the original data used in the phylogenetic analyses of Dietrich et al. (Insect Systematics and Diversity, in review). The text file is marked up according to the standard NEXUS format commonly used by various phylogenetic analysis software packages. The file will be parsed automatically by a variety of programs that recognize NEXUS as a standard bioinformatics file format. The complete taxon names corresponding to the 131 genus names listed under “BEGIN TAXA” are listed in Table 1 in the included PDF file “Taxa_and_characters”; the 229 morphological characters (names abbreviated under under “BEGIN CHARACTERS” are fully explained in the list of character descriptions following Table 1 in the same PDF). The data matrix follows “MATRIX” and gives the numerical values of characters for each taxon. Question marks represent missing data. The lists of characters and taxa and details on the methods used for phylogenetic analysis are included in the submitted manuscript.

The following files include specimen information, DNA sequence data, and additional information on the analyses used to reconstruct the phylogeny of the leafhopper genus Neoaliturus as described in the Methods section of the original paper: 1. Taxon_sampling.csv: contains data on the individual specimens from which DNA was extracted, including sample code, taxon name, collection data (locality, date and name of collector) and museum unique identifier. 2. Alignments.zip: a ZIP archive containing 432 separate FASTA files representing the aligned nucleotide sequences of individual gene loci used in the analysis. 3. Concatenated_Matrix.fa: is a FASTA file containing the concatenated individual gene alignments used for the maximum likelihood analysis in IQ-TREE. 4. Genes_and_Loci.rtf: identifies the individual genes and loci used in the analysis. The partition name is the same as the name of the individual alignment file in the zipped Alignments folder. 5. Partitions_best_scheme.nex: is a text file in the standard NEXUS format that indicates the names of the individual data partitions and their locations in the concatenated matrix, and also indicates the substitution model for each partition. 6. (New in this version 2) Scripts & Description.zip includes 8 custom shell or perl scripts used to assemble the DNA sequence data by perform reciprocal blast searches between the reference sequences and assemblies for each sample, extract the best sequences based on the blast searches, screen the hits for each locus and keep only the best result, and generate the nucleotide sequence dataset for the predicted orthologues (see the file description.txt for details). 7. (New in this version 2) Full_genetic_distances_matrix.csv shows the genetic distances between pairs of samples in the datset (proportion of nucleotides that differ between samples).