cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26942182.v2
title: Impact of risk factors on the incidence of tunneled dialysis catheter infections: a systematic review and meta-analysis

description: Tunneled dialysis catheters (TDCs) are important for hemodialysis in patients awaiting a permanent surgical solution, kidney transplantation or without feasible surgical access. Infection of a TDC is a common and severe complication, which often requires removal of the TDC and causes high morbidity and mortality. To date, several risk factors for TDC infections have been reported. This systematic review and meta-analysis aim to provide an overview of currently known risk factors. A systematic literature search was conducted, including all studies describing patient-, catheter-, and dialysis-related risk factors for TDC infections. In case sufficient data was available for a risk factor, a meta-analysis with random effects model was performed. Out of 1273 studies, 30 were included describing a total of 71 risk factors. A meta-analysis was conducted for 26 risk factors. The average incidence of TDC infections was 1.16 ± 0.70/1000 catheter days. Diabetes (odds ratio, OR 1.96), coronary artery disease (OR 2.16), peripheral artery disease (OR 2.28), history of sepsis (OR 2.79), and the number of prior TDCs (OR 1.24) were the most significant risk factors for infection. Several risk factors are associated with increased TDC infection rates. Most of these risk factors are also linked with infection in other populations and most likely reflect the general frailty of hemodialysis patients. The association between many risk factors and TDC infections was often unclear due to the low number of studies available. Additional large cohort studies are necessary to demonstrate the relevance of these risk factors.
DOI: 10.7483/opendata.cms.o4e0.smn5
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.ddb9rg
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is List of invasive or harmful alien species in Wallis and Futuna Territory"
 ]
}

 The dataset includes 128 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072619-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.4bto.3gck
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.li4n.cyo3
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.mi7o.izh8
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.o9a4.lsgv
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.syyaqb
title: Occurrence Download

description: A dataset containing 11122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Serbia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Grus grus (Linnaeus, 1758)"
 ]
}

 The dataset includes 11122 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072618-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.isvq.117j
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.yiqj.p3yi
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.as3fr3
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Serbia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Podiceps auritus (Linnaeus, 1758)"
 ]
}

 The dataset includes 113 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072620-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.k2d5.p7fn
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.17632/m7nxy3747f.1
title: Data for: Legacy of warming and cover crops on the response of soil microbial function to repeated drying and rewetting cycles.

description: We examined the legacy of warming and cover crops on the response of soil microbial function to repeated drying and rewetting cycles. We introduced open top chambers to warm the soil surface of a field plot experiment in which cover crops (single species monocultures and 4-species polycultures) were grown over the summer after harvest and before planting of autumn sown cash crops in a cereal rotation. Soil samples were collected from warmed and ambient areas of the experimental plots in spring, before harvesting the cereal crop. We quantified respiration (a measure of soil microbial function) with high-frequency CO2 flux measurements after 0, 1, 2, 4, or 8 wet/dry cycles imposed in the laboratory and the addition of barley grass powder substrate at a ratio of 10 mg g-1 soil.
DOI: 10.17632/c5t2wbpn5w.1
title: LDHU3_16.1320

description: Protein of unknown function (DUF3184); Leishmania donovani (HU3 strain)
DOI: 10.7483/opendata.cms.51bq.94ao
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.x18o.7azb
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p0ol.8nyo
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.0t94.g1gn
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.hgy8.hbvu
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.3fx2.gdiu
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17028/rd.lboro.13645832
title: Artificial Symbol Learning With Training - Experiment 1 Data analysis

description: Zip file containing all data and analysis files for Experiment 1 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors
DOI: 10.17028/rd.lboro.13645832.v1
title: Artificial Symbol Learning With Training - Experiment 1 Data analysis

description: Zip file containing all data and analysis files for Experiment 1 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors
DOI: 10.17028/rd.lboro.13645847
title: Artificial Symbol Learning With Training - Experiment 1 materials

description: Materials for Experiment 1 in the paper "Learning Artificial Number Symbols with Ordinal and Magnitude Information".Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors<br>
DOI: 10.17028/rd.lboro.13645850
title: Artificial Symbol Learning With Training - Experiment 2 Data analysis

description: Zip file containing all data and analysis files for Experiment 2 in:Weiers, H., Inglis, M., & Gilmore, C. (under review). Learning artificial number symbols with ordinal and magnitude information.Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors
DOI: 10.17028/rd.lboro.13645853
title: Artificial Symbol Learning With Training - Experiment 2 materials

description: Materials for Experiment 2 in the paper "Learning Artificial Number Symbols with Ordinal and Magnitude Information".Article abstractThe question of how numerical symbols gain semantic meaning is a key focus of mathematical cognition research. Some have suggested that symbols gain meaning from magnitude information, by being mapped onto the approximate number system, whereas others have suggested symbols gain meaning from their ordinal relations to other symbols. Here we used an artificial symbol learning paradigm to investigate the effects of magnitude and ordinal information on number symbol learning. Across two experiments, we found that after either magnitude or ordinal training, adults successfully learned novel symbols and were able to infer their ordinal and magnitude meanings. Furthermore, adults were able to make relatively accurate judgements about, and map between, the novel symbols and non-symbolic quantities (dot arrays). Although both ordinal and magnitude training was sufficient to attach meaning to the symbols, we found beneficial effects on the ability to learn and make numerical judgements about novel symbols when combining small amounts of magnitude information for a symbol subset with ordinal information about the whole set. These results suggest that a combination of magnitude and ordinal information is a plausible account of the symbol learning process.© The Authors
DOI: 10.7483/opendata.cms.bnk7.4n4g
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.aq3k.jibs
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.5ngr.tw47
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.oj6v.oqoj
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.0tam.1ccs
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-55_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.zz6u.yze7
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.bfsm37
title: Occurrence Download

description: A dataset containing 1716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Serbia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tringa glareola Linnaeus, 1758"
 ]
}

 The dataset includes 1716 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072621-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.ujw0.iawk
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.i5nw.7lax
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nn1a.84cs
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-0cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6pjo.evr5
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/xch32gm2xr.1
title: Length-weight parameters of coral reef fishes from the Sulu Archipelago

description: This dataset includes detailed information on the LWR parameters and growth patters of 161 species from 60 genera and 21 families, making it the most extensive study of its kind in the Philippines. Additionally, we highlight the discovery of new total length records for 27 species and the documentation of the first LWR records for 73 marine fish species in the Philippines.
DOI: 10.7483/opendata.cms.q6w6.myoz
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/jh9g67mw3j.1
title: Invasive Alien Plant (IAP) detection

description: This dataset contains the revised codes used to create the Figures in my review article on the detection of Invasive Alien Plant (IAP) species using remote sensing, machine learning, and deep learning techniques. The software used to produce the code for the charts was the RStudio 2022.07.0+548 "Spotted Wakerobin" Release (34ea3031089fa4e38738a9256d6fa6d70629c822, 2022-07-06) for Windows, Build 548, 2009-2022, PBC. Excel tables, for codes requiring data tables, have also been uploaded, including a file outlining the parameters used to carry out the codes.
DOI: 10.7483/opendata.cms.1xoq.cg2f
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.x0cd.9ly5
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-1000cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4nk5.2mcp
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ppzy.a4ns
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6pxp.wuil
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.pbg5.e0le
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-100cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.qnte.m0w5
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nidg.jufq
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5mgn.jypf
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.00b7.og2f
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-10cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.43lk.coy1
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.3l2u.w0qk
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-20cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.1tl3.eps2
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.1nqo.6q5v
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-20cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.vnxz.rypn
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.tcsy.80nj
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-50cm_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.f49q.2s8r
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xr70.ek41
title: /WplusH_HTo2LongLivedTo4G_MH-125_MFF-7_ctau-50cm_TuneCP5_fixedLifetime_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.thrp.62so
title: /WplusH_HToBB_WToLNu_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.1gl7.na36
title: /WplusH_HToBB_WToLNu_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.74o6.yyki
title: /WplusH_HToBB_WToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.0ie3.o0gh
title: /WplusH_HToBB_WToQQ_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.24yt.udd7
title: /WplusH_HToCC_WToLNu_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.qm3k.qpy2
title: /WplusH_HToCC_WToLNu_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.gnih.84c3
title: /WplusH_HToCC_WToQQ_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.d1rw.cyyg
title: /WplusH_HToCC_WToQQ_M125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.y5b4.klpk
title: /WplusH_HToGG_WToAll_M120_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.b5oh.9gxv
title: /WplusH_HToGG_WToAll_M120_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM