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There are two files in this dataset. File1: AffiNorm AffiNorm contains 1,001 rows, including one header row, randomly sampled from MapAffil 2018 Dataset ([**https://doi.org/10.13012/B2IDB-2556310_V1**](https://databank.illinois.edu/datasets/IDB-2556310)). Each row in the file corresponds to a particular author on a particular PubMed record, and contains the following 26 columns, comma-delimited. All columns are ASCII, except city which contains Latin-1. COLUMN DESCRIPTION 1. PMID: the PubMed identifier. int. 2. ORDER: the position of the author. int. 3. YEAR - The year of publication. int(4), eg: 1975. 4. affiliation - affiliation string of the author. eg: Department of Pathology, University of Chicago, Illinois 60637. 5. annotation_type: the number of institutions annotated, denoted by S, M, O, or Z, where "S" (single) indicates 1 institution was annotated; "M" (Multiple) indicates more than one institutions were annotated; "O" (Out of Vocabulary or None) indicates no institution was annotated, but an institution was apparently mentioned; "Z" indicates no institution was mentioned. 6. Institution: the standard name(s) of the annotated institution(s), according to ROR. if "S" (single institution), it is saved as a string, eg: University of Chicago; if "M", it is saved as a string that looks like a python list, eg: ['Public Health Laboratory Service'; 'Centre for Applied Microbiology and Research']; if "O" or "Z", then blank. 7. inst_type: the type of institution, according to ROR. the potential values are: education, funder, healthcare, company, archive, nonprofit, government, facility, other. An institution may have more than one type, eg: ['Education', 'Funder'] 8. type_edu: TRUE if the inst_type contains "Education"; FALSE otherwise. 9. RORid: ROR identifier(s), eg: https://ror.org/05hs6h993. when multiple, the order corresponds to institution (column 6) 10. RORid_label. the standard name(s) of the annotated institution(s) according to ROR.same as institution (column 6) 11. GRIDid: GRID identifier(s). eg: grid.170205.1 12. GRIDid_label: the standard name(s) of the annotated institution(s) according to GRID. eg: University of Chicago. 13. WikiDataid: WikiData identifier(s). eg: Q131252 14. WikiDataid_label: the standard name(s) of the annotated institution(s) according to WikiData. eg: University of Chicago 15. synonyms: a comma separated list of variant names from InsVar (file 2) . format of string. eg: University of Chicago, Chicago University, U of C, UChicago, uchicago.edu, U Chicago, ... 16. MapAffil-grid: GRID from the MapAffil 2018 Dataset. 17. MapAffil-grid_label: The standard name of institution from MapAffil 2018 Dataset. 18. judge_mapA: TRUE if GRIDid (column 11) contains MapAffil-grid (column 16); FALSE otherwise. 19. MapAffiltemporal-grid: GRID from the temporal version of MapAffil, http://abel.ischool.illinois.edu/data/MapAffilTempo2018.tsv.gz 20. MapAffiltemporal-grid_label: The standard name of institution from MapAffilTemporal 2018 Dataset. 21. judge_mapT: TRUE if GRIDid (column 11) contains MapAffiltemporal-grid (column 19); FALSE otherwise. 22. RORapi_query_id: ROR from ROR api tool (query endpoint) 23. RORapi_query_id_label: The standard name of institution from ROR api tool (query endpoint). format in string. 24. judge_rorapi_affiliation: TRUE if RORid (column 9) contains RORapi_query_id (column 22); FALSE otherwise. 25. rorapi_affiliation_id: ROR from ROR api tool (affiliation endpoint). 26. judge_rorapi_affiliation: TRUE if RORid (column 9) contains RORapi_affiliation (column 25); FALSE otherwise. File 2: insVar.json InsVar is a supplementary dataset for AffiNorm, which includes the institution ID and its redirected aliases from wikidata. The institution ID list is from GRID, the redirected aliases are from wiki api, for example: https://en.wikipedia.org/wiki/Special:WhatLinksHere?target=University+of+Illinois+Urbana-Champaign&namespace=&hidetrans=1&hidelinks=1&limit=100 In InsVar, the data is saved in a python dictionary format. the key is the GRID identifier, for example: "grid.1001.0" (Australian National University), and the value is a list of redirected aliases strings. {"grid.1001.0": ["ANU", "ANU College", "ANU College of Arts and Social Sciences", "ANU College of Asia and the Pacific", "ANU Union", "ANUSA", "Asia Pacific Week",    "Australia National University", "Australian Forestry School", "the Australian National University", ...], "grid.1002.3": ...}

This dataset contains all data and supplementary materials from "Improving precision and accuracy of genetic mapping with genotyping-by-sequencing data in outcrossing species". An Excel file a list of all QTLs and linkage group length (in cM) obtained with two different SNP-calling methods (Tassel-Uneak and Tassel-GBS), genetic map-construction method (linkage-only and reference order-corrected) and depth filters (12x, 20x, 30x and 40x) for genetic mapping of 18 biomass yield traits in a biparental Miscanthus sinensis population using RAD-Seq SNPs is provided as "Supplementary file 1". A Perl script with the code for filtering VCF and HapMap-formatted data files is provided as “Supplementary file 2”. Phenotype data used for QTL mapping is provided as “Supplementary File 3”. A Perl script with the code for the simulation study is provided as “Supplementary file 4”.

This is an Excel file containing data about the physical environments of four Brazilian schools and the average daily minutes/day of physical activity and sedentary behavior exhibited by schoolchildren during school hours. The Following Key describes the basic variables: Subject IDs and Characteristics Subject_ID: ID of Subject total_days: Total number of days subject participated in experiment Gender : Gender of subject Age: Age of subject School IDs and Characteristics ID_School = ID of School school1 = 1 if ID_School = 1, else = 0 school2 = 1 if ID_School = 2, else = 0 school3 = 1 if ID_School = 3, else = 0 school4 = 1 if ID_School = 4, else = 0 TotalSiteArea: Total Site Area on School Campus PatioArea: Area of Patio(s) CourtyardArea: Area of Courtyard(s) TotalOpenArea: Total Area of Open Spaces on Campus Class: Number of Sections in the School Population: Total Number of Students Enrolled in the School

Enzymatic reduction of oxyanions such as sulfite (SO32−) requires the delivery of multiple electrons and protons, a feat accomplished by cofactors tailored for catalysis and electron transport. Replicating this strategy in protein scaffolds may expand the range of enzymes that can be designed de novo. Mirts et al. selected a scaffold protein containing a natural heme cofactor and then engineered a cavity suitable for binding a second cofactor—an iron-sulfur cluster (see the Perspective by Lancaster). The resulting designed enzyme was optimized through rational mutation into a catalyst with spectral characteristics and activity similar to that of natural sulfite reductases.

SCiLS MSI data files, images used in the figures and table contents for the tables found in the manuscript. The figures are labeled by figure and by their title on each figure set, including those found in the Supplementary Information. The tables are in an MS Excel sheet with the corresponding contents. The tables list the metabolites found in the images. To reduce the number of images in the manuscript, the tables complete the metabolite information not observed in the images. The images can be found using the SCiLS data files. A software license is needed to open these files. The SCiLS data files contains the processed MSI data for all obtained images. All files in the corresponding SCiLS data file must be present to open the individual data file. The feature list used for MSI analysis should be saved on the attached bookmark inside the SCiLS file so it should be available once the file is opened. SCiLS files can only be opened with the Bruker SCiLS software. If using an outdated version (before Version 13.01.17218), the files may not open or show poor quality.

Data for the publication of Magnetic Fields in the Pillars of Creation (Sarkar et al.). Contains the fits files and python scripts.

Bioenergy crops have potential for being a sustainable and renewable feedstock for biofuels and various value-added bioproducts. The study utilizes recently developed transgenic sugarcane (“oilcane”) bagasse for chemical-free coproduction of high-value bioproducts, i.e., furfurals, HMF, acetic acid, cellulosic sugars, and vegetative lipids. Hydrothermal pretreatment was optimized at 210 °C for 5 min to coproduce 6.91%, 2.67%, 5.07%, 2.42% and 37.82% (w/w) furfurals, HMF, acetic acid, vegetative lipids, and cellulosic sugars, respectively from lignocellulosic biomass. Additionally, nanofiltration system in-series was successfully established to recover sugars, furfurals, HMF, and acetic acid from the pretreatment liquor. 1st nanofiltration with Duracid NF membrane rejected ∼99% sugars. Concentrated sugars with significantly reduced inhibitory products were obtained in retentate for fermentation. 2nd nanofiltration with NF90 membrane used permeate of 1st nanofiltration as feed and rejected ∼ 86% furfurals. The work demonstrates the feasibility of coproducing and recovering multiple biochemicals from lignocellulosic biomass.

This dataset consists of all the files that are part of the manuscript titled "Evidence for a robust sign-changing s-wave order parameter in monolayer films of superconducting Fe(Se,Te)/Bi2Te3". For detailed information on the individual files refer to the readme file.

This dataset provides microhabitat and environmental variables collected in the habitat of the poison frog Mantella baroni from 155 1-meter square quadrats in Vohimana Reserve along forest valleys, on slopes, and on ridgelines. We also provide data from photographic capture-recapture surveys used for estimating abundance.

This dataset contains both processed and raw data that were leveraged to conduct analysis presented fully in the report "Community Vulnerability Assessment for Electric Vehicle Travelers Responsive to Extreme Flooding" and partially in the under review paper "Vulnerability Assessment of Electric Vehicles and their Charging Station Network during Evacuations".

Over the past 15 years, soybean seed yield response to season-long elevated O3 concentrations [O3] and to year-to-year weather conditions was studied using free-air O3 concentration enrichment (O3-FACE) in the field at the SoyFACE facility in Central Illinois. Elevated [O3] significantly reduced seed yield across cultivars and years. However, our results quantitatively demonstrate that weather conditions, including soil water availability and air temperature, did not alter yield sensitivity to elevated [O3] in soybean.

Dataset associated with Jones and Ward JAE-2020-0031.R1 submission: Pre-to post-fledging carryover effects and the adaptive significance of variation in wing development for juvenile songbirds. Excel CSV files with data used in analyses and file with descriptions of each column. The flight ability variable in this dataset was derived from fledgling drop tests, examples of which can be found in the related dataset: Jones, Todd M.; Benson, Thomas J.; Ward, Michael P. (2019): Flight Ability of Juvenile Songbirds at Fledgling: Examples of Fledgling Drop Tests. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-2044905_V1.

Information on the location, dimensions, time of treefall or death, decay state, wood nutrient, wood pH and wood density data, and soil moisture, slope, distance from forest edge and soil nutrient data associated with the publication "Interspecific wood trait variation predicts decreased carbon residence time in changing forests" authored by Sierra Perez, Jennifer Fraterrigo, and James Dalling. ** <b>Note:</b> Blank cells indicate that no data were collected.

These are similarity matrices of countries based on dfferent modalities of web use. Alexa website traffic, trending vidoes on Youtube and Twitter trends. Each matrix is a month of data aggregated

This data was collected between 2004 and 2010 at White River National Wildlife Refuge (WRNWR) and Saint Francis National Forest (SF). It was collected as part of two master’s and one PhD project at Arkansas State University USA studying Swainson’s Warbler habitat use, survival, and body condition.

Dataset for Food availability influences angling vulnerability in muskellunge

Supplemental data for the paper titled 'Environmental modulators of algae-bacteria interactions at scale'. Each of the excel workbooks corresponding to datasets 1, 2, and 3 contain a README sheet explaining the reported data. Dataset 4 comprising microscopy data contains a README text file describing the image files.

Trained models for multi-task multi-dataset learning for sequence tagging in tweets. Sequence tagging tasks include POS, NER, Chunking, and SuperSenseTagging. Models were trained using: <a href="https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py">https://github.com/socialmediaie/SocialMediaIE/blob/master/SocialMediaIE/scripts/multitask_multidataset_experiment.py</a> See <a href="https://github.com/socialmediaie/SocialMediaIE">https://github.com/socialmediaie/SocialMediaIE</a> and <a href="https://socialmediaie.github.io">https://socialmediaie.github.io</a> for details. If you are using this data, please also cite the related article: Shubhanshu Mishra. 2019. Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets. In Proceedings of the 30th ACM Conference on Hypertext and Social Media (HT '19). ACM, New York, NY, USA, 283-284. DOI: https://doi.org/10.1145/3342220.3344929

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.

Traces created using DeathStarBench (https://github.com/delimitrou/DeathStarBench) benchmark of microservice applications with injected failures on containers. Failures consist of disk/CPU/memory failures.

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

Dataset associated with Merrill et al. ECE-2021-05-00793.R1 submission: Early life patterns of growth are linked to levels of phenotypic trait covariance and post-fledging mortality across avian species. Excel CSV files with all of the data used in analyses and file with descriptions of each column.

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.