cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12724958.v2
title: Baja SAE: avaliação ergonômica utilizando a termografia infravermelha

description: Utilizando a termografia infravermelha para a coleta de dados, este trabalho técnico teve como objetivo a avaliação do comportamento térmico do <i>cockpit</i> (assento e encosto) do protótipo de corrida <i>off-road</i> UFSC Baja SAE durante funcionamento (motor de combustão ligado). A partir dessa avaliação, o trabalho visou verificar se a estrutura existente entre o motor e o cockpit do UFSC Baja SAE cumpria satisfatoriamente a sua função de isolar o calor emitido pelo motor durante seu funcionamento, possibilitando segurança e conforto ao piloto.
DOI: 10.5281/zenodo.3951613
title: Single-cell metabolic profiling (scMEP) of human cytotoxic T cells

description: Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, <em>in vitro</em> T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon. All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment) MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results
description: {"references": ["Hartmann, FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology. https://doi.org/10.1038/s41587-020-0651-8", "Hartmann, FJ et al. (2020) Multiplexed Single-cell Metabolic Profiles Organize the Spectrum of Cytotoxic Human T Cells. bioRxiv. https://doi.org/10.1101/2020.01.17.909796"]}
DOI: 10.5281/zenodo.3951612
title: Single-cell metabolic profiling (scMEP) of human cytotoxic T cells

description: Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, <em>in vitro</em> T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon. All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment) MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results
description: {"references": ["Hartmann, FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology. https://doi.org/10.1038/s41587-020-0651-8", "Hartmann, FJ et al. (2020) Multiplexed Single-cell Metabolic Profiles Organize the Spectrum of Cytotoxic Human T Cells. bioRxiv. https://doi.org/10.1101/2020.01.17.909796"]}
DOI: 10.6084/m9.figshare.12922337.v1
title: R0195_20171014a_videos

description: Rat 0195, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.7910/dvn/skzy5j/se8zox
title: Parliamentary Questions in Turkish Parliaments (2018-2020).tab

description: :unav
DOI: 10.5281/zenodo.8210183
title: Dataset for: Star Photometry for DECaLS and SDSS Images Based on Convolutional Neural Networks

description: The dataset consists of two parts, the training dataset and the comparison dataset. The training dataset and the comparison dataset are each divided into three parts, namely the simulation dataset, the SDSS dataset and the DECaLs dataset. The simulation dataset is simulated by PhoSim software and the original size of the simulation data is 512x512 pixels. The SDSS dataset is from DR12 and the ObjId of the target as well as other parameters are given in the csv file.The DECaLs dataset is from DR9.
DOI: 10.5281/zenodo.8210182
title: Dataset for: Star Photometry for DECaLS and SDSS Images Based on Convolutional Neural Networks

description: The dataset consists of two parts, the training dataset and the comparison dataset. The training dataset and the comparison dataset are each divided into three parts, namely the simulation dataset, the SDSS dataset and the DECaLs dataset. The simulation dataset is simulated by PhoSim software and the original size of the simulation data is 512x512 pixels. The SDSS dataset is from DR12 and the ObjId of the target as well as other parameters are given in the csv file.The DECaLs dataset is from DR9.
DOI: 10.34740/kaggle/dsv/6581309
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.34740/kaggle/dsv/6581336
title: BBC News
DOI: 10.5281/zenodo.8384826
title: Electrospray ionization mass spectra of PI3KC3-C1 bound nucleotides

description: Raw data file of GTP mass spectra from publication.
DOI: 10.5281/zenodo.8384825
title: Electrospray ionization mass spectra of PI3KC3-C1 bound nucleotides

description: Raw data file of GTP mass spectra from publication.
DOI: 10.5281/zenodo.8384839
title: Trypsin digest mass spec analysis from PI3KC3-C1

description: Raw and processed data for tryptic digestion of PI3KC3-C1, showing myristoylated peptide from VPS15(1-29).
DOI: 10.5281/zenodo.8384838
title: Trypsin digest mass spec analysis from PI3KC3-C1

description: Raw and processed data for tryptic digestion of PI3KC3-C1, showing myristoylated peptide from VPS15(1-29).
DOI: 10.6084/m9.figshare.24224737
title: Supplementary Tables

description: The datasets presented include five tables of size statistics of sulfides, Raman band parameters for organic matter, and C-O, S and Fe isotope data for the Doushantuo Formation in Weng’an, South China.
DOI: 10.6084/m9.figshare.24224737.v1
title: Supplementary Tables

description: The datasets presented include five tables of size statistics of sulfides, Raman band parameters for organic matter, and C-O, S and Fe isotope data for the Doushantuo Formation in Weng’an, South China.
DOI: 10.34740/kaggle/dsv/6581568
title: WallStreetBets 2022
DOI: 10.5281/zenodo.8395849
title: Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation

description: Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44). This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.
DOI: 10.5281/zenodo.8395848
title: Supplementary Materials: ImpactX Modeling of Benchmark Tests for Space Charge Validation

description: Supplementary materials (aka data artifact or data archive) for our HB2023 publication: " ImpactX Modeling of Benchmark Tests for Space Charge Validation" (Paper ID: THBP44). This work was supported by the Director, Office of Science of the U.S. Department of Energy under Contracts No. DE-AC02-05CH11231 and DE-AC02-07CH11359. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.
DOI: 10.26180/24195345
title: Data and code for the paper SCAR and the Antarctic Treaty System – an evidence-based perspective

description: Details of and counts of Working Papers and Information Papers submitted to the Antarctic Treaty Consultative Papers from 1961 to 2023, including the names of the authors (country Parties, Observers, Experts) along with metadata describing the contents of the files and R code used for the analyses of these data. All data were collected and analysed as described in the associated text file over the period August to September 2023.
DOI: 10.26180/24195345.v1
title: Data and code for the paper SCAR and the Antarctic Treaty System – an evidence-based perspective

description: Details of and counts of Working Papers and Information Papers submitted to the Antarctic Treaty Consultative Papers from 1961 to 2023, including the names of the authors (country Parties, Observers, Experts) along with metadata describing the contents of the files and R code used for the analyses of these data. All data were collected and analysed as described in the associated text file over the period August to September 2023.
DOI: 10.17190/amf/2006959
title: AmeriFlux FLUXNET-1F CA-Obs Saskatchewan - Western Boreal, Mature Black Spruce

description: This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-Obs Saskatchewan - Western Boreal, Mature Black Spruce. This is the FLUXNET version of the carbon flux data for the site CA-Obs Saskatchewan - Western Boreal, Mature Black Spruce produced by applying the standard ONEFlux (1F) software. Site Description - 53.98717° N, 105.11779° W, elavation of 628.94 m, BOREAS 1994, 1996, BERMS climate measurements began Dec. 1996 and flux measurements in Apr. 1999
DOI: 10.17190/amf/2006971
title: AmeriFlux FLUXNET-1F US-Ced Cedar Bridge

description: This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ced Cedar Bridge. This is the FLUXNET version of the carbon flux data for the site US-Ced Cedar Bridge produced by applying the standard ONEFlux (1F) software. Site Description - Wildfires and prescribed fires are a common occurrence in the NJ Pinelands. Prior to a 1995 nonstand replacing wildfire, the stand was last burned in the very large wildfire in 1963. Plow lines were installed for fire control in December of 2007, followed by a prescribed burns in 2008 and 2013, conducted by the New Jersey Forest Fire Service. Pine looper defoliated the stand in 1998, and Gypsy moth defoliated the understory and deciduos oaks in 2007. Pitch Pines are largely unaffected by defoliation by Gypsy moth.
DOI: 10.6084/m9.figshare.24199467
title: Self-carbon-thermal-reduction strategy for boosting the Fenton-like activity of single Fe-N<sub>4</sub> sites by carbon-defect engineering

description: The data of Manuscript “Self-carbon-thermal-reduction strategy for boosting the Fenton-like activity of single Fe-N<sub>4</sub> sites by carbon-defect engineering”.
DOI: 10.6084/m9.figshare.24199467.v1
title: Self-carbon-thermal-reduction strategy for boosting the Fenton-like activity of single Fe-N<sub>4</sub> sites by carbon-defect engineering

description: The data of Manuscript “Self-carbon-thermal-reduction strategy for boosting the Fenton-like activity of single Fe-N<sub>4</sub> sites by carbon-defect engineering”.
DOI: 10.5281/zenodo.8372886
title: Data to form periodic lossless ternary seeds of maximum weight (Part 2)

description: Data to form periodic lossless ternary seeds of maximum weight.<br> <br> Detailed information can be found in the GitHub project (https://github.com/vtman/perlotSeeds). Codes to generate periodic blocks (binary and ternary) can also be found there.<br> <br> Binary seeds can have only two symbols (0 = "do not care" = "_" or 1 = "match" = "#"). The length of a seed is the number of its elements, weight of a seed is the number of its 1-elements. The goal is to find seeds of maximum weight, so they can be used when there are two strings with a given number of mismatches. It is observed that in many cases, these seeds of maximum weight have a periodic structure: the same block is repeated multiple times + its remainder. Blocks for binary seeds can be found with the help of the PerFSeeB project (https://github.com/vtman/PerFSeeB). These blocks have the maximum possible weight. <br> <br> In genetics, we have four symbols in sequences (A, C, G, T). However, the chance of having a pointwise mutation is not the same. for any pairs. A <strong>transition</strong> mutation (A ↔ G or C ↔ T) is often twice higher than a <strong>transversion</strong> mutation (A ↔ C, A ↔ T, G ↔ C, G ↔ T). Transition-constrained seeds use ternary alphabet {<strong>#</strong>, <strong>@</strong>, <strong>_</strong>} where <strong>@</strong> is for a match or a transition mismatch. To generate ternary seeds, we first need to generate ternary blocks. These ternary blocks can be found when we use binary blocks. However, we sometimes need to use binary blocks for less than the maximum weight.<br> <br> T4V2.zip and T7V2.zip are in this dataset.
DOI: 10.5281/zenodo.8372887
title: Data to form periodic lossless ternary seeds of maximum weight (Part 2)

description: Data to form periodic lossless ternary seeds of maximum weight.<br> <br> Detailed information can be found in the GitHub project (https://github.com/vtman/perlotSeeds). Codes to generate periodic blocks (binary and ternary) can also be found there.<br> <br> Binary seeds can have only two symbols (0 = "do not care" = "_" or 1 = "match" = "#"). The length of a seed is the number of its elements, weight of a seed is the number of its 1-elements. The goal is to find seeds of maximum weight, so they can be used when there are two strings with a given number of mismatches. It is observed that in many cases these seeds of maximum weight have a periodic structure: the same block is repeated multiple times + its remainder. Blocks for binary seeds can be found with the help of the PerFSeeB project (https://github.com/vtman/PerFSeeB). These blocks have the maximum possible weight. <br> <br> In genetics, we have four symbols in sequences (A, C, G, T). However, the chance of having a pointwise mutation is not the same. for any pairs. A <strong>transition</strong> mutation (A ↔ G or C ↔ T) is often twice higher than a <strong>transversion</strong> mutation (A ↔ C, A ↔ T, G ↔ C, G ↔ T). Transition-constrained seeds use ternary alphabet {<strong>#</strong>, <strong>@</strong>, <strong>_</strong>} where <strong>@</strong> is for a match or a transition mismatch. To generate ternary seeds we first need to generate ternary blocks. These ternary blocks can be found when we use binary blocks. However, sometimes we need to use binary blocks for less than the maximum weight.<br> <br> T2V4.zip and T2V7.zip are in this dataset.<br> <br> The other datasets are in 10.5281/zenodo.8370909
DOI: 10.5281/zenodo.8395813
title: Data to form periodic lossless ternary seeds of maximum weight (Part 2)

description: Data to form periodic lossless ternary seeds of maximum weight.<br> <br> Detailed information can be found in the GitHub project (https://github.com/vtman/perlotSeeds). Codes to generate periodic blocks (binary and ternary) can also be found there.<br> <br> Binary seeds can have only two symbols (0 = "do not care" = "_" or 1 = "match" = "#"). The length of a seed is the number of its elements, weight of a seed is the number of its 1-elements. The goal is to find seeds of maximum weight, so they can be used when there are two strings with a given number of mismatches. It is observed that in many cases, these seeds of maximum weight have a periodic structure: the same block is repeated multiple times + its remainder. Blocks for binary seeds can be found with the help of the PerFSeeB project (https://github.com/vtman/PerFSeeB). These blocks have the maximum possible weight. <br> <br> In genetics, we have four symbols in sequences (A, C, G, T). However, the chance of having a pointwise mutation is not the same. for any pairs. A <strong>transition</strong> mutation (A ↔ G or C ↔ T) is often twice higher than a <strong>transversion</strong> mutation (A ↔ C, A ↔ T, G ↔ C, G ↔ T). Transition-constrained seeds use ternary alphabet {<strong>#</strong>, <strong>@</strong>, <strong>_</strong>} where <strong>@</strong> is for a match or a transition mismatch. To generate ternary seeds, we first need to generate ternary blocks. These ternary blocks can be found when we use binary blocks. However, we sometimes need to use binary blocks for less than the maximum weight.<br> <br> T4V2.zip and T7V2.zip are in this dataset.
DOI: 10.6084/m9.figshare.23705673.v3
title: Supplemental Tables. Lipolysis inhibition as a treatment of clinical ketosis in dairy cows: Enhancing adipose tissue metabolic and immune functions

description: Table S1 Primers used for gene expression analysis in adipose tissue from dairy cows Table S2 antibodies used in flow cytometry to characterize macrophage phenotype in adipose tissues from dairy cows
DOI: 10.25625/jpywgg/tgxxav
title: ex-ante-covid19-risk.tif

description: :unav
DOI: 10.25625/jpywgg
title: Ex Ante Risks of COVID-19 in Indonesia

description: Data set on the Ex-ante spatial risk of COVID-19 in Indonesia. A product of the publication: Seufert, J. D., A. Python, C. Weisser, E. Cisneros, K. Kis-Katos, and T. Kneib (2022): Mapping ex-ante spatial risks of COVID-19 in Indonesia using a Bayesian geostatistical model on airport network data. Journal of the Royal Statistical Society Series A (Statistics in Society), 185: 2121–2155.
DOI: 10.25345/c5k35mq0k
title: MassIVE MSV000091067 - Periphylla associated microbiota_2020
title: Extracts of selected microbial community associated with the mesopelagic Periphylla periphylla jellyfish
DOI: 10.34740/kaggle/dsv/6575503
title: Barcode & QR
DOI: 10.34740/kaggle/dsv/6582288
title: 📒Meta Kaggle ported to SQLITE DB
DOI: 10.34740/kaggle/dsv/6582385
title: 📒 Meta Kaggle ported to MS SQL SERVER 2022 DB
DOI: 10.5066/p9418s5i
title: Biological Indicators and Mercury Concentrations in Smallmouth Bass

description: Biological indicators including morphometric (length, weight), age, sex and health indicators including organismal (health assessment index, condition factor), organ (hepatosomatic index, gonadosomatic index), cellular (intersex, parasite density, macrophage aggregate density), subcellular (plasma vitellogenin, estradiol, testosterone, 11-keto testosterone) and molecular (hepatic transcript abundance) were measured in smallmouth bass from tributaries in the Chesapeake Bay watershed. Total mercury was measured in these fish and correlations between mercury and biological indicators were assessed.
DOI: 10.5066/p9fwtkzq
title: Radiocarbon age dating of biological material from cores collected off central California in 1999, 2006, and 2019

description: Results of radiocarbon age dating of planktic and benthic foraminifera collected from cores obtained in 1999, 2006, and 2019 offshore central California in the vicinity of Morro Bay.
DOI: 10.5066/p91csh0p
title: Data for Characterizing Changes in the 1-percent Annual Exceedance Probability Streamflows for Climate Change Scenarios in the Housatonic River Watershed, Massachusetts, Connecticut, and New York

description: The U.S. Geological Survey in cooperation with the Federal Emergency Management Agency has conducted a study to evaluate potential changes to1-percent annual exceedance probability (AEP) streamflows. The study was conducted using the Precipitation Runoff Modeling System (PRMS). Climate inputs to the model of temperature and precipitation were scaled to anticipated changes that could occur in 2030, 2050, and 2100 based on global climate models. The output from the models were used to characterize the 1-percent AEP streamflows for the years 2030, 2050, and 2100 and compare the results to baseline conditions, 1950-2015. The data include the model input and output and spatial data for model referencing. Scripts for processing PRMS output to obtain final results are also provided.
DOI: 10.5066/p9xpwumi
title: Data for Estimating Peak-Flow Quantiles for Selected Annual Exceedance Probabilities in Illinois

description: The U.S. Geological Survey Central Midwest Water Science Center completed a report (Over and others, 2023) documenting the methods, results, and applications of an updated flood-frequency study for the State of Illinois. This data release contains data related to the analysis completed to determine peak-flow quantiles (flood frequency estimates) at streamgages in Illinois for 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs), as well as data used to develop regional regression equations that relate the peak-flow quantiles and the basin characteristics of selected streamgages in Illinois, Indiana, and Wisconsin, based on data through water year 2017 (a water year is the period from October 1 to September 30 and is designated by the year in which it ends; for example, water year 2017 was from October 1, 2016, to September 30, 2017). There is one set of equations for each of Illinois’ seven hydrologic regions, and, for each region, one equation for each AEP. These regional equations allow the estimation of peak-flow quantiles at ungaged locations throughout the state and their uncertainties. This data release contains a figure of the study area in addition to seven tables that provide both input data and results in tabular format. See metadata for detailed information.
DOI: 10.5066/p9d6xdij
title: Specific conductance and other groundwater quality data, Siskiyou Pass area, southwestern Oregon, 2018 to 2021

description: Specific conductance (SC), estimated chloride (Cl), and other major ion data sets used in an analysis of the extent to which deicer applications affect groundwater quality in the Siskiyou Pass area, southwestern Oregon, 2018 to 2021. The analysis is documented in the following publication: Gingerich, S.B., Wise, D.R., and Stonewall, A.J., 2023, Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021: U.S. Geological Survey Scientific Investigations Report 2023–5107, 39 p., https://doi.org/10.3133/sir20235107. This data release contains seven child items: 1) Specific conductance and major ions measured at selected sampling locations during July 30 to August 1, 2018, including springs, streams, and wells, in the Siskiyou Pass area, southwestern Oregon.2) Measured specific conductance, water temperature, pH, and major ions at selected sampling locations from September 2019 to November 2020, including springs, streams, and a well, in the Siskiyou Pass area, southwestern Oregon. 3) List of selected sampling locations from July 2018 to February 2021, including springs, streams, wells, and a pond, in the Siskiyou Pass area, southwestern Oregon. 4) List of selected sites that were part of the study in the Siskiyou Pass area, southwestern Oregon. 5) Measured specific conductance, water temperature, Quantab units and estimated chloride concentration from National Water Quality Lab and Quantab field chloride titrators at selected sampling locations from July 2018 to November 2020, including springs, streams, and a well, in the Siskiyou Pass area, southwestern Oregon. 6) Mean annual salt and deicer chemical applications from winter 2016-17 through winter 2019-20. 7) Quality assurance data at selected locations, Oregon.