cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.90el.s5oz
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.9pe4tz
title: Occurrence Download

description: A dataset containing 660 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.30759 31.92971,42.34749 27.11004,37.46138 38.03714,2.18551 50.25711,-6.30759 31.92971))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Siganus rivulatus Forsskål & Niebuhr, 1775",
 "Year 2000-2025"
 ]
}

 The dataset includes 660 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072429-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p2rbr
title: Occurrence Download

description: A dataset containing 3813 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Living Specimen)",
 "Continent is Europe",
 "Country is Spain",
 "Geometry POLYGON((-6.90549209091688 39.988460331172746,-2.90549209091688 39.988460331172746,-2.90549209091688 43.988460331172746,-6.90549209091688 43.988460331172746,-6.90549209091688 39.988460331172746))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Neovison vison (Schreber, 1777)",
 "Year 1920-2025"
 ]
}

 The dataset includes 3813 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072428-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8421746
title: EUParlspeech: A Dataset of Over 1 Million References to European Integration in Parliamentary Speeches

description: As part of my PhD dissertation I developed EUParlspeech, a dataset of over 1 million references to European integration made in the plenary debates of ten national parliaments between 1989 and 2019. It is built from existing datasets of parliamentary speeches, most notably Parlspeech (Rauh and Schwalbach 2020). The dataset has applications for scholars of EU integration, party competition, political communication, and international relations. This chapter in my dissertation explains the construction of the dataset, describes its features, and demonstrates its face, convergent, and predictive validity. Automated analysis of parties' EU statements in parliament yield meaningful and well-known cross party differences, with challenger parties more likely to send clearer, more sceptical cues on integration than mainstream parties. Moreover, these automated measures correlate highly with expert assessments (CHES) and - in the case of the UK's Conservative Party - individual MPs' ideal point estimates based on EU statements in plenary debates can predict their subsequent vote and position at the 2016 referendum. I conclude that EUParlspeech data provide a promising new approach to studying party contestation over European integration.
DOI: 10.5281/zenodo.8421747
title: EUParlspeech: A Dataset of Over 1 Million References to European Integration in Parliamentary Speeches

description: As part of my PhD dissertation I developed EUParlspeech, a dataset of over 1 million references to European integration made in the plenary debates of ten national parliaments between 1989 and 2019. It is built from existing datasets of parliamentary speeches, most notably Parlspeech (Rauh and Schwalbach 2020). The dataset has applications for scholars of EU integration, party competition, political communication, and international relations. This chapter in my dissertation explains the construction of the dataset, describes its features, and demonstrates its face, convergent, and predictive validity. Automated analysis of parties' EU statements in parliament yield meaningful and well-known cross party differences, with challenger parties more likely to send clearer, more sceptical cues on integration than mainstream parties. Moreover, these automated measures correlate highly with expert assessments (CHES) and - in the case of the UK's Conservative Party - individual MPs' ideal point estimates based on EU statements in plenary debates can predict their subsequent vote and position at the 2016 referendum. I conclude that EUParlspeech data provide a promising new approach to studying party contestation over European integration.
DOI: 10.15468/dl.46cfhc
title: Occurrence Download

description: A dataset containing 7616 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((36.24502 2.182148, 37.26409 2.182148, 37.26409 3.374535, 36.24502 3.374535, 36.24502 2.182148))"
 ]
}

 The dataset includes 7616 records from 88 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072430-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.puuqtk
title: Occurrence Download

description: A dataset containing 3432211 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 3432211 records from 303 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072418-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14671447
title: Dataset for "Analysis and Prediction of Addiction among Virtual Reality Users"

description: Abstract:[Objective] To understand the addiction situation and influencing factors of virtual reality users, and provide reference basis for timely and effective prevention and intervention of user addiction. [Methods] Based on a questionnaire survey, univariate analysis, multivariate analysis, and model prediction were conducted on the data of 1164 participants in VR related Facebook groups and Reddit subedits. [Results] The single factor analysis results show that the user's own attributes, usage duration, perception level, and application types of virtual reality devices can significantly affect the degree of addiction; The results of multivariate analysis showed that the age of users, the number of days used per week, the number of hours used per day, and the perceived level of the device can significantly affect the probability of addiction. In addition, this study used decision tree algorithm to predict adolescent virtual reality device addiction, with a prediction accuracy of 0.957. [Conclusion] The addiction of virtual reality users is related to multiple factors such as gender, age, usage time, application type, and perception level. When developing VR applications and content, consideration should be given to balancing user immersion and healthy use, and reasonable control of usage time is also an effective means to prevent VR addiction.

Keywords:Virtual reality applications; Addiction; Regression analysis; Prediction model
DOI: 10.5281/zenodo.14671448
title: Dataset for "Analysis and Prediction of Addiction among Virtual Reality Users"

description: Abstract:[Objective] To understand the addiction situation and influencing factors of virtual reality users, and provide reference basis for timely and effective prevention and intervention of user addiction. [Methods] Based on a questionnaire survey, univariate analysis, multivariate analysis, and model prediction were conducted on the data of 1164 participants in VR related Facebook groups and Reddit subedits. [Results] The single factor analysis results show that the user's own attributes, usage duration, perception level, and application types of virtual reality devices can significantly affect the degree of addiction; The results of multivariate analysis showed that the age of users, the number of days used per week, the number of hours used per day, and the perceived level of the device can significantly affect the probability of addiction. In addition, this study used decision tree algorithm to predict adolescent virtual reality device addiction, with a prediction accuracy of 0.957. [Conclusion] The addiction of virtual reality users is related to multiple factors such as gender, age, usage time, application type, and perception level. When developing VR applications and content, consideration should be given to balancing user immersion and healthy use, and reasonable control of usage time is also an effective means to prevent VR addiction.

Keywords:Virtual reality applications; Addiction; Regression analysis; Prediction model
DOI: 10.7483/opendata.cms.123b.5skz
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14671258
title: Retaining volunteers
DOI: 10.5281/zenodo.14671259
title: Retaining volunteers
DOI: 10.7483/opendata.cms.yv15.j9wo
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.c7e2.3c1o
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.r0uv.x8a3
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15125/bath-01369
title: Premises of Support and Opposition to NATO Enlargement: A Dataset

description: In this NVivo 12 textual analysis package files and Stata 17 datasets, the focus is on the arguments presented by 40 leading analysts of European security in their arguments for their view on whether NATO enlargement was a mistake. The selection of the experts was done by the Foreign Affairs journal that published an opinion survey of 62 experts of which 40 published their arguments in addition to their opinions in this survey publication of Foreign Affairs (Foreign Affairs Survey 2022). These arguments by 40 of the 62 surveyed experts is the textual material of this dataset. The textual data was coded to reveal premises of these experts. The coding was based on distinctions that the creator of this dataset created on the basis of literature reviewed in his article “Theoretical Premises of Support of and Opposition to NATO Enlargement.”
description: The data was compiled by using NVivo 12 textual analysis package to code texts and measure levels of various theoretical premises. The quantitative results of the textual analysis were then statistically analysed by using Stata 17 program package.
description: NVivo 12 textual analysis package and Stata 17 statistical analysis package.
DOI: 10.7483/opendata.cms.pjgy.xi6p
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14671470
title: Europe Commercial Aircraft Market 2024 To 2033

description: Europe Commercial Aircraft Market Size, Trends and Insights By Aircraft Type (Narrow-body Aircraft, Wide-body Aircraft, Regional Aircraft), By Technology (Conventional Aircraft, Advanced Aircraft), By Engine Type (Turbofan, Turboprop), By Application (Passenger Aircraft, Freighter), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the Europe Commercial Aircraft Market is expected to record a CAGR of 5.8% from 2023 to 2032. In 2023, the market size is projected to reach a valuation of USD 556.6 Million. By 2032, the valuation is anticipated to reach USD 924.5 Million.

The Europe Commercial Aircraft Market encompasses the production, sale, and operation of aircraft designed for passenger and cargo transportation. Key players like Airbus and Boeing dominate, producing a range of aircraft types, from narrow-body to wide-body planes. Historical trends include a focus on fuel efficiency, technological advancements, and passenger experience.

Presently, the market is characterized by a strong emphasis on sustainability, accelerated digital transformation, and innovative solutions to address challenges posed by the COVID-19 pandemic. The industry strives for operational efficiency, safety, and environmental responsibility in shaping the future of air travel in Europe.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=38682
DOI: 10.5281/zenodo.14671471
title: Europe Commercial Aircraft Market 2024 To 2033

description: Europe Commercial Aircraft Market Size, Trends and Insights By Aircraft Type (Narrow-body Aircraft, Wide-body Aircraft, Regional Aircraft), By Technology (Conventional Aircraft, Advanced Aircraft), By Engine Type (Turbofan, Turboprop), By Application (Passenger Aircraft, Freighter), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the Europe Commercial Aircraft Market is expected to record a CAGR of 5.8% from 2023 to 2032. In 2023, the market size is projected to reach a valuation of USD 556.6 Million. By 2032, the valuation is anticipated to reach USD 924.5 Million.

The Europe Commercial Aircraft Market encompasses the production, sale, and operation of aircraft designed for passenger and cargo transportation. Key players like Airbus and Boeing dominate, producing a range of aircraft types, from narrow-body to wide-body planes. Historical trends include a focus on fuel efficiency, technological advancements, and passenger experience.

Presently, the market is characterized by a strong emphasis on sustainability, accelerated digital transformation, and innovative solutions to address challenges posed by the COVID-19 pandemic. The industry strives for operational efficiency, safety, and environmental responsibility in shaping the future of air travel in Europe.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=38682
DOI: 10.7483/opendata.cms.imy6.bfge
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.jpvi.kmsn
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-50_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.57932/f127607d-3687-40d4-b0ef-98fba6aeb781
title: Input Images for the article : "Self-supervised learning based clustering workflow for exploring seismological data from dense networks"

description: This repository contains a dataset of 1,966,581 images representing 30-second waveform segments and their corresponding spectrograms, derived from the continuous seismic record of the "Marie-sur-Tinée" landslide dataset. The "Marie-sur-Tinée" landslide dataset was recorded from April 18, 2021, to June 28, 2021, and includes data from 69 seismic stations. The 1.9 million images generated from the data of 13 stations were used to train a Self-Supervised Learning (SSL) model which was later used to encode the entire dataset. The clustering results based on the embedding returned by the model are presented in the article (submitted): Rimpot J., Hibert C., Malet J.-P., Forestier G., and Weber J., Self-supervised Learning Based Clustering Workflow for Exploring Seismological Data from Dense Networks
DOI: 10.7483/opendata.cms.bh9q.y5zo
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.agr4.5g8e
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.2qky.u52w
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4ms9.tkxb
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.jtvp.ozrf
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5fil.vmqq
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yzoj.d2k7
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.syxm.k9p5
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.c541.kygg
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.fmnz.q3kn
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.qzqu.qhoc
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.iyns.c1ti
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fqeg.h3or
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.nnbu.8vr7
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tbon.fu4r
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.k3bj.r9gg
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.2ulc.x3q2
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.2366.qtsp
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.vuto.pmky
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.azo9.o0fl
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.i0cf.iv6b
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.w0o6.cfdy
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.hjfc.wbmm
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.d3g0.irid
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.s1cl.qlna
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kacj.91ci
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.vrcb.13vc
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5kwm.vbcs
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cv2d.omr7
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.57745/7gm8es
title: Blue_data.zip

description: :unav
DOI: 10.57745/e0vnuc
title: Readme.md

description: :unav
DOI: 10.57745/k4i4wk
title: WhiteCar.sbf

description: :unav
DOI: 10.57745/u17odn
title: BlueCar.sbf

description: :unav
DOI: 10.7483/opendata.cms.mwyf.050o
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6ews.lx1b
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.57745/aijcfm
title: White_data.zip

description: :unav
DOI: 10.57745/dm1kxs
title: Grey_data.zip

description: :unav
DOI: 10.57745/wcztdy
title: UbloxSpoofedCar1.mat

description: :unav
DOI: 10.7483/opendata.cms.96y1.qfwd
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ee69.1ily
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kesz.w3id
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4zuu.9uj4
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.28r0.x102
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.t375.kn0w
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4psl.tt1k
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6grb.izns
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.9on0.u79b
title: /WminusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM