cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27683196.v5
title: ASI-GCNss

description: Limited borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods, such as ordinary kriging (OK) and inverse distance weighting (IDW), may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. To overcome this challenge, we propose an adaptive spatial interpolation graph convolutional network (ASI-GCN) model. This model synergistically integrated the principles of spatial auto-correlation with the capabilities of graph convolutional networks (GCN). By merging these elements, the ASI-GCN model effectively constrained the transfer of pollutant concentrations. It adeptly captures nuanced variations in spatial structure, thereby enhancing the precision of soil pollution characterization. We applied our model to a coking plant in Beijing based on 215 soil samples collected from 15 boreholes. To evaluate the robustness of the model, three pollutants with distinct volatilization characteristics were employed, including arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). The leave-one-out cross-validation procedure was used to estimate the performance of ASI-GCN and its derivatives, ASI-GCN_RC_G and ASI-GCN_RC_K, when they are used to make predictions at unsampled sites.The results showed that the ASI-GCN model demonstrated improved performance with R² values of 0.52, 0.67, and 0.57, and RMSE values of 5.041 mg/kg, 1.699 mg/kg , and 167.710 mg/kg for As, BaP, and Ben, respectively, outperforming traditional models like OK and IDW. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, semi-volatile BaP concentrated in the silty sand layer, while volatile Ben was predominantly found in the clay and fine sand layers. The ASI-GCN model provides key insights for risk assessment and remediation strategies by capturing complex contaminant distributions with limited borehole data, demonstrating its potential for application in various contaminated sites.
DOI: 10.6084/m9.figshare.27683196.v4
title: ASI-GCN

description: Limited borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods, such as ordinary kriging (OK) and inverse distance weighting (IDW), may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. To overcome this challenge, we propose an adaptive spatial interpolation graph convolutional network (ASI-GCN) model. This model synergistically integrated the principles of spatial auto-correlation with the capabilities of graph convolutional networks (GCN). By merging these elements, the ASI-GCN model effectively constrained the transfer of pollutant concentrations. It adeptly captures nuanced variations in spatial structure, thereby enhancing the precision of soil pollution characterization. We applied our model to a coking plant in Beijing based on 215 soil samples collected from 15 boreholes. To evaluate the robustness of the model, three pollutants with distinct volatilization characteristics were employed, including arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). The leave-one-out cross-validation procedure was used to estimate the performance of ASI-GCN and its derivatives, ASI-GCN_RC_G and ASI-GCN_RC_K, when they are used to make predictions at unsampled sites.The results showed that the ASI-GCN model demonstrated improved performance with R² values of 0.52, 0.67, and 0.57, and RMSE values of 5.041 mg/kg, 1.699 mg/kg , and 167.710 mg/kg for As, BaP, and Ben, respectively, outperforming traditional models like OK and IDW. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, semi-volatile BaP concentrated in the silty sand layer, while volatile Ben was predominantly found in the clay and fine sand layers. The ASI-GCN model provides key insights for risk assessment and remediation strategies by capturing complex contaminant distributions with limited borehole data, demonstrating its potential for application in various contaminated sites.
DOI: 10.7483/opendata.cms.ne8h.b6og
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.m60j.l83h
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.vlvx.y4mc
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ihdq.2gxa
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.didy.5mup
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.fnrr.uouy
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.n71a.x56w
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.balg.2vq2
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.yu71.3b4u
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.3hfc.7y8x
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.fydm.g1g2
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.50tq.cqmt
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.xb0d.kkl0
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.wtpa.6j93
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-450_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.tiv3.9g66
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.8il9.4yhc
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.jscn.465j
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xe7m.qk6q
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.eaik.u2vp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.62ru.77vw
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.bg1s.zkzg
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.j038.pooz
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.oi4d.ug6m
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.f50g.m26g
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.x8bp.ppxk
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.gjc3.9347
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.ctsd.u9qa
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qlls.hjum
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.9m5l.0tws
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y9s6.gaeo
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.122n.fojc
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.673b.e6el
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.r2lj.qjof
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.rsqp.4sh3
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.0iwr.2ajk
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.vynz.3glk
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.12vi.l6n4
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.faio.h5vm
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.uobo.lnhm
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ox5i.6pxp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.4xea.6xd6
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.snr3.5tzj
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.27nm.hbit
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.s3bg.se6s
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.vl55.gqji
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y8zq.rrsd
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.yyqa.xfyx
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ayff.rzp5
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.q8je.ig4o
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.42e3.tdnb
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.qov3.7ots
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.oc1v.u8x1
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-550_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.fnzx.tyo8
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.8dxa.f8sa
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.0eiq.yg3e
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.lssz.jwyj
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.cznv.6xso
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.p6gt.lcz0
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.dnyt.2tqq
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.iyfq.s5pb
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.15468/dl.8gyqnm
title: Occurrence Download

description: A dataset containing 1229 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Barcant Butterfly Collection - Riodinidae"
 ]
}

 The dataset includes 1229 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069965-241126133413365/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.69bn.rx4v
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.lbzm.t2on
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.tulw.cnas
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.46ej.9hr5
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.qmtt.ktou
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.msb4.7qcr
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.mrwg.n6hh
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.5281/zenodo.14642998
title: labels

description: labels of the labeled images
DOI: 10.5281/zenodo.8223380
title: labels

description: labels of the labeled images
DOI: 10.5281/zenodo.8223381
title: labels

description: labels of the labeled images
DOI: 10.7483/opendata.cms.03z4.pg86
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.6084/m9.figshare.27542466.v2
title: The supporting data for paper “<b>E</b><b>feffect of Particle Size on Stick-Slip </b><b>of Shear Bands Considering </b><b>Randomness of Initial Particle locations in </b><b>Discrete Element</b><b> Modeling</b>”

description: This is the supporting data for paper “<b>E</b><b>feffect of Particle Size on Stick-Slip </b><b>of Shear Bands Considering </b><b>Randomness of Initial Particle locations in </b><b>Discrete Element</b><b> Modeling</b>”. We have made public the data of all the figures, and our research results can be reproduced according to the names of the figures
DOI: 10.7483/opendata.cms.pljg.btvv
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kow1.889e
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.0urm.xylv
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y2mg.88ye
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-600_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.2saj.281v
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.gxrk.ysp2
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.42g8.a2xx
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.9kgf.jglg
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.vc0b.dofu
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.sek6.kquk
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.4zvj.cfph
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.7ojr.g3bp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.jbha.wydb
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.skbc.ejar
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.x2k5.mzcj
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.kxtzxu
title: Occurrence Download

description: A dataset containing 691 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Barcant Butterfly Collection - Lycaenidae"
 ]
}

 The dataset includes 691 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069970-241126133413365/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.eib6.gya3
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.oro7.pym1
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.6byb.l7ha
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.ysgj.04t6
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.65zp.3im4
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.2lyq.b4fp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.3cf6.oikp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.sdj2.fur3
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ygys.99kp
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.4r3l.x9h4
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ankf.bsk9
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-650_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.aohc.30bj
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-700_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.33vv.rfm2
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-700_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.touj.fo5s
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-700_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.duuh.2n60
title: /NMSSM_XToYHTo2Z2BTo2L2Nu2B_MX-700_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM