Datasets

Baseline data from a multi-modal intervention study conducted at the University of Illinois at Urbana-Champaign. Data include results from a cardiorespiratory fitness assessment (maximal oxygen consumption, VO2max), a body composition assessment (Dual-Energy X-ray Absorptiometry, DXA), and Magnetic Resonance Spectroscopy Imaging. Data set includes data from 435 participants, ages 18-44 years.

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/

Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying Our approach is described in our paper titled: Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020). The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020 NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.

Data sets to reproduce the results provided by the tutorial in paper "Data-Driven and Machine Learning Based Framework for Image-Guided Single-Cell Mass Spectrometry"

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 (2020) "Continued post-retraction citation of a fraudulent clinical trial report, eleven years after it was retracted for falsifying data".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

# WikiCSSH If you are using WikiCSSH please cite the following: > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. “WikiCSSH: Extracting Computer Science Subject Headings from Wikipedia.” In Workshop on Scientific Knowledge Graphs (SKG 2020). https://skg.kmi.open.ac.uk/SKG2020/papers/HAN_et_al_SKG_2020.pdf > Han, Kanyao; Yang, Pingjing; Mishra, Shubhanshu; Diesner, Jana. 2020. "WikiCSSH - Computer Science Subject Headings from Wikipedia". University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-0424970_V1 Download the WikiCSSH files from: https://doi.org/10.13012/B2IDB-0424970_V1 More details about the WikiCSSH project can be found at: https://github.com/uiuc-ischool-scanr/WikiCSSH This folder contains the following files: WikiCSSH_categories.csv - Categories in WikiCSSH WikiCSSH_category_links.csv - Links between categories in WikiCSSH Wikicssh_core_categories.csv - Core categories as mentioned in the paper WikiCSSH_category_links_all.csv - Links between categories in WikiCSSH (includes a dummy category called <ROOT> which is parent of isolates and top level categories) WikiCSSH_category2page.csv - Links between Wikipedia pages and Wikipedia Categories in WikiCSSH WikiCSSH_page2redirect.csv - Links between Wikipedia pages and Wikipedia page redirects in WikiCSSH This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a> or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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

Datasets used in the study, "TRACTION: Fast non-parametric improvement of estimated gene trees," accepted at the Workshop on Algorithms in Bioinformatics (WABI) 2019.

This dataset was created based on the publicly available microdata from PNS-2019, a national health survey conducted by the Instituto Brasileiro de Geografia e Estatistica (IBGE, Brazilian Institute of Geography and Statistics). IBGE is a federal agency responsible for the official collection of statistical information in Brazil – essentially, the Brazilian census bureau. Data on selected variables focusing on biopsychosocial domains related to pain prevalence, limitations and treatment are available. The Fundação Instituto Oswaldo Cruz has detailed information about the PNS, including questionnaires, survey design, and datasets (www.pns.fiocruz.br). The microdata can be found on the IBGE website (https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html?caminho=PNS/2019/Microdados/Dados).

This datasets provide basis of our analysis in the paper - Revising the Ozone Depletion Potentials for Short-Lived Chemicals such as CF3I and CH3I. All datasets here are from the model output (CAM4-chem). All the simulations (background and perturbation) were run to steady-state and only the last year outputs used in analysis are archived here.

The XSEDE program manages the database of allocation awards for the portfolio of advanced research computing resources funded by the National Science Foundation (NSF). The database holds data for allocation awards dating to the start of the TeraGrid program in 2004 to present, with awards continuing through the end of the second XSEDE award in 2021. The project data include lead researcher and affiliation, title and abstract, field of science, and the start and end dates. Along with the project information, the data set includes resource allocation and usage data for each award associated with the project. The data show the transition of resources over a fifteen year span along with the evolution of researchers, fields of science, and institutional representation.

This repository contains the training dataset associated with the 2023 Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics (DGM-Image Challenge), hosted by the American Association of Physicists in Medicine. This dataset contains more than 100,000 8-bit images of size 512x512. These images emulate coronal slices from anthropomorphic breast phantoms adapted from the VICTRE toolchain [1], with assigned X-ray attenuation coefficients relevant for breast computed tomography. Also included are the labels indicating the breast type. The challenge has now concluded. More information about the challenge can be found here: <a href="https://www.aapm.org/GrandChallenge/DGM-Image/">https://www.aapm.org/GrandChallenge/DGM-Image/</a>. * New in V3: we added a CSV file containing the image breast type labels and example images (PNG).

Author-ity 2009 baseline dataset. Prepared by Vetle Torvik 2009-12-03 The dataset comes in the form of 18 compressed (.gz) linux text files named authority2009.part00.gz - authority2009.part17.gz. The total size should be ~17.4GB uncompressed. &bull; How was the dataset created? The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in July 2009. A total of 19,011,985 Article records and 61,658,514 author name instances. Each instance of an author name is uniquely represented by the PMID and the position on the paper (e.g., 10786286_3 is the third author name on PMID 10786286). Thus, each cluster is represented by a collection of author name instances. The instances were first grouped into "blocks" by last name and first name initial (including some close variants), and then each block was separately subjected to clustering. Details are described in <i>Torvik, V., & Smalheiser, N. (2009). Author name disambiguation in MEDLINE. ACM Transactions On Knowledge Discovery From Data, 3(3), doi:10.1145/1552303.1552304</i> <i>Torvik, V. I., Weeber, M., Swanson, D. R., & Smalheiser, N. R. (2005). A Probabilistic Similarity Metric for Medline Records: A Model for Author Name Disambiguation. Journal Of The American Society For Information Science & Technology, 56(2), 140-158. doi:10.1002/asi.20105</i> Note that for Author-ity 2009, some new predictive features (e.g., grants, citations matches, temporal, affiliation phrases) and a post-processing merging procedure were applied (to capture name variants not capture during blocking e.g. matches for subsets of compound last name matches, and nicknames with different first initial like Bill and William), and a temporal feature was used -- this has not yet been written up for publication. &bull; How accurate is the 2009 dataset (compared to 2006 and 2009)? The recall reported for 2006 of 98.8% has been much improved in 2009 (because common last name variants are now captured). Compared to 2006, both years 2008 and 2009 overall seem to exhibit a higher rate of splitting errors but lower rate of lumping errors. This reflects an overall decrease in prior probabilites -- possibly because e.g. a) new prior estimation procedure that avoid wild estimates (by dampening the magnitude of iterative changes); b) 2008 and 2009 included items in Pubmed-not-Medline (including in-process items); and c) and the dramatic (exponential) increase in frequencies of some names (J. Lee went from ~16,000 occurrences in 2006 to 26,000 in 2009.) Although, splitting is reduced in 2009 for some special cases like NIH funded investigators who list their grant number of their papers. Compared to 2008, splitting errors were reduced overall in 2009 while maintaining the same level of lumping errors. &bull; What is the format of the dataset? The cluster summaries for 2009 are much more extenstive than the 2008 dataset. Each line corresponds to a predicted author-individual represented by cluster of author name instances and a summary of all the corresponding papers and author name variants (and if there are > 10 papers in the cluster, an identical summary of the 10 most recent papers). Each cluster has a unique Author ID (which is uniquely identified by the PMID of the earliest paper in the cluster and the author name position. The summary has the following tab-delimited fields: 1. blocks separated by '||'; each block may consist of multiple lastname-first initial variants separated by '|' 2. prior probabilities of the respective blocks separated by '|' 3. Cluster number relative to the block ordered by cluster size (some are listed as 'CLUSTER X' when they were derived from multiple blocks) 4. Author ID (or cluster ID) e.g., bass_c_9731334_2 represents a cluster where 9731334_2 is the earliest author name instance. Although not needed for uniqueness, the id also has the most frequent lastname_firstinitial (lowercased). 5. cluster size (number of author name instances on papers) 6. name variants separated by '|' with counts in parenthesis. Each variant of the format lastname_firstname middleinitial, suffix 7. last name variants separated by '|' 8. first name variants separated by '|' 9. middle initial variants separated by '|' ('-' if none) 10. suffix variants separated by '|' ('-' if none) 11. email addresses separated by '|' ('-' if none) 12. range of years (e.g., 1997-2009) 13. Top 20 most frequent affiliation words (after stoplisting and tokenizing; some phrases are also made) with counts in parenthesis; separated by '|'; ('-' if none) 14. Top 20 most frequent MeSH (after stoplisting; "-") with counts in parenthesis; separated by '|'; ('-' if none) 15. Journals with counts in parenthesis (separated by "|"), 16. Top 20 most frequent title words (after stoplisting and tokenizing) with counts in parenthesis; separated by '|'; ('-' if none) 17. Co-author names (lowercased lastname and first/middle initials) with counts in parenthesis; separated by '|'; ('-' if none) 18. Co-author IDs with counts in parenthesis; separated by '|'; ('-' if none) 19. Author name instances (PMID_auno separated '|') 20. Grant IDs (after normalization; "-" if none given; separated by "|"), 21. Total number of times cited. (Citations are based on references extracted from PMC). 22. h-index 23. Citation counts (e.g., for h-index): PMIDs by the author that have been cited (with total citation counts in parenthesis); separated by "|" 24. Cited: PMIDs that the author cited (with counts in parenthesis) separated by "|" 25. Cited-by: PMIDs that cited the author (with counts in parenthesis) separated by "|" 26-47. same summary as for 4-25 except that the 10 most recent papers were used (based on year; so if paper 10, 11, 12... have the same year, one is selected arbitrarily)

Prepared by Vetle Torvik 2018-04-15 The dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed. &bull; How was the dataset created? First and last names of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including Ethnea+Genni as described in: <i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA. http://hdl.handle.net/2142/88927</i> <i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i> EthnicSeer: http://singularity.ist.psu.edu/ethnicity <i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i> SexMachine 0.1.1: <a href="https://pypi.python.org/pypi/SexMachine/">https://pypi.org/project/SexMachine</a> First names, for some Author-ity records lacking them, were harvested from outside bibliographic databases. &bull; The code and back-end data is periodically updated and made available for query at <a href ="http://abel.ischool.illinois.edu">Torvik Research Group</a> &bull; What is the format of the dataset? The dataset contains 9,300,182 rows and 10 columns 1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition) 2. name: full name used as input to EthnicSeer) 3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX 4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction 5. lastname: used as input for Ethnea+Genni 6. firstname: used as input for Ethnea+Genni 7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short) 8. Genni: predicted gender; 'F', 'M', or '-' 9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male) 10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'

This annotation study compared RobotReviewer's data extraction to that of three novice data extractors, using six included articles synthesized in one Cochrane review: Bailey E, Worthington HV, van Wijk A, Yates JM, Coulthard P, Afzal Z. Ibuprofen and/or paracetamol (acetaminophen) for pain relief after surgical removal of lower wisdom teeth. Cochrane Database Syst Rev. 2013; CD004624; doi:10.1002/14651858.CD004624.pub2 The goal was to assess the relative advantage of RobotReviewer's data extraction with respect to quality.

We have recently created an approach for high throughput single cell measurements using matrix assisted laser desorption / ionization mass spectrometry (MALDI MS) (J Am Soc Mass Spectrom. 2017, 28, 1919-1928. doi: 10.1007/s13361-017-1704-1. Chemphyschem. 2018, 19, 1180-1191. doi: 10.1002/cphc.201701364). While chemical detail is obtained on individual cells, it has not been possible to correlate the chemical information with canonical cell types. Now we combine high-throughput single cell mass spectrometry with immunocytochemistry to determine lipid profiles of two known cell types, astrocytes and neurons from the rodent brain, with the work appearing as “Lipid heterogeneity between astrocytes and neurons revealed with single cell MALDI MS supervised by immunocytochemical classification” (DOI: 10.1002/anie.201812892). Here we provide the data collected for this study. The dataset provides the raw data and script files for the rodent cerebral cells described in the manuscript.

Data: Variation in pesticide toxicity in the western honey bee (Apis mellifera) associated with consuming phytochemically different monofloral honeys Includes: Identification and quantification of phenolic components of honeys: Raw_data_JOCE.xlsx – sheet: “HoneyPhytochemicals” Effects of honey phytochemicals on acute pesticide toxicity: Raw_data_JOCE.xlsx – sheet: “raw_LD50 Raw_data_JOCE.xlsx – sheet: “raw_LD50_hive_based”

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.

The dataset contains trajectories of Pt nanoparticles in 1.98 mM NaBH4 and NaCl, tracked under liquid-phase TEM. The coordinates (x, y) of nanoparticles are provided, together with the conversion factor that translates pixel size to actual distance. In the file, ∆t denotes the time interval and NaN indicates the absence of a value when the nanoparticle has not emerged or been tracked. The labeling of nanoparticles in the paper is also noted in the second row of the file.

A defining feature of the Anthropocene is the distortion of the biosphere phosphorus (P) cycle. A relatively sudden acceleration of input fluxes without a concomitant increase in output fluxes has led to net accumulation of P in the terrestrial-aquatic continuum. Over the past century, P has been mined from geological deposits to produce crop fertilizers. When P inputs are not fully removed with harvest of crop biomass, the remaining P accumulates in soils. This residual P is a uniquely anthropogenic pool of P, and its management is critical for agronomic and environmental sustainability. This dataset includes data for us to quantify residual P from different long-term managed systems. The following is the desccription of the dataset. There are 7 sheets in total. 1. P_balance: From Morrow Plots maize-maize rotaiton (1888-2021), L: Low estimation; M: medium estimation; H: high estimation; 2. M3P: From Morrow Plots selected plots (selected years), M3P_sur: Mehlich III P concentration in surface 17cm soils; M3P_sub: Mehlich III P concentration in 17-34cm subsoils; P_balance: the difference between P inputs and P outputs; TP_sur: total P stocks in surface 17cm soils; TP_sub: total P stocks in 17-34cm subsoils; 3. Morrow_Plot_P_pool_all: Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; Fertilized: P stocks in the fertilized plot; Unfertilized: P stocks in the unfertilized plot; F-U: difference between P stocks in ther fertilized and unfertilized plots; dif%: percent difference in total P; 4. Rothamsted_P_pool_all: Treatment: Unfertilized: no fertilization; FYM: farmyard manure; PK: synthetic P and K fertilizer; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 5. L'Acadie_P_pool_all: Treatment: MP_LowP: moldboard plow with low rate of P fertilizer; MP_HighP: moldboard plow with high rate of P fertilizer; NT_LowP: no till with low rate of P fertilizer; NT_HighP: no till with high rate of P fertilizer; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 6. Rothamsted_P_pool_duration: Treatment: Unfertilized: no fertilization; FYM: farmyard manure; PK: synthetic P and K fertilizer; Duration: from a year to another year; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P; 7. L'Acadie_P_pool_duration: Treatment: MP_LowP: moldboard plow with low rate of P fertilizer; MP_HighP: moldboard plow with high rate of P fertilizer; NT_LowP: no till with low rate of P fertilizer; NT_HighP: no till with high rate of P fertilizer; Duration: from a year to another year; Group: a - labile P; b - Fe/Al-P; c - Ca-P; d - total organic P; e - non-extractable P; P_change: differnce in P stocks over time; dif%: percent difference in total P;

This dataset provides estimates of total Irrigation Water Use (IWU) by crop, county, water source, and year for the Continental United States. Total irrigation from Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) is provided for 20 crops and crop groups from 2008 to 2020 at the county spatial resolution. In total, there are nearly 2.5 million data points in this dataset (3,142 counties; 13 years; 3 water sources; and 20 crops). This dataset supports the paper by Ruess et al (2024) "Total irrigation by crop in the Continental United States from 2008 to 2020", Scientific Data, doi: 10.1038/s41597-024-03244-w When using, please cite as: Ruess, P.J., Konar, M., Wanders, N., and Bierkens, M.F.P. (2024) Total irrigation by crop in the Continental United States from 2008 to 2020, Scientific Data, doi: 10.1038/s41597-024-03244-w

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 prior to March 10, 2023 (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 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).

This dataset contains records of four years of taxi operations in New York City and includes 697,622,444 trips. Each trip records the pickup and drop-off dates, times, and coordinates, as well as the metered distance reported by the taximeter. The trip data also includes fields such as the taxi medallion number, fare amount, and tip amount. The dataset was obtained through a Freedom of Information Law request from the New York City Taxi and Limousine Commission. The files in this dataset are optimized for use with the ‘decompress.py’ script included in this dataset. This file has additional documentation and contact information that may be of help if you run into trouble accessing the content of the zip files.

Dataset associated with Jones et al. GCB-23-1273.R1 submission: Phenotypic signatures of urbanization? Resident, but not migratory, songbird eye size varies with urban-associated light pollution levels. Excel CSV file with all of the data used in analyses and file with descriptions of each column.