cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7483/opendata.cms.krip.y5ii
title: /SMS-TChiWg_mChi-1000_mLSP-1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v3/NANOAODSIM
DOI: 10.7483/opendata.cms.i0il.4wrx
title: /SMS-TChiWH-mNLSP1000_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y7w0.v8kz
title: /SMS-TChiWH-mNLSP1000_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cxpr.kxvh
title: /SMS-TChiWH-mNLSP400_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ag1l.dhzt
title: /SMS-TChiWH-mNLSP400_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.stp1.j6hh
title: /SMS-TChiWZ_ZToLL_mZMin-0p1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.l9ax.wsyy
title: /SMS-TChiWZ_ZToLL_mZMin-0p1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.y2z4.d23f
title: /SMS-TChiZZ-mNLSP1000_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.v7rd.4n0p
title: /SMS-TChiZZ-mNLSP1000_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.jhol.9ifa
title: /SMS-TChiZZ-mNLSP400_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.yaoh.y01k
title: /SMS-TChiZZ-mNLSP400_mLSP1_TuneCP5_13TeV-madgraphMLM-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5xhd.gpp0
title: /SPS_Charmonium_JPsiToMuMu_TuneCP5_13TeV-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qgvd.qmru
title: /SPS_Charmonium_JPsiToMuMu_TuneCP5_13TeV-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.foq8.c7e6
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17_ext1-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.fwj7.tli0
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17_ext2-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5ftc.pu3y
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17_ext3-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.9plt.9a4b
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17_ext4-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.8vpj.nu7z
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.bi8k.cbe5
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17_ext1-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.or3d.mlq9
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17_ext2-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.mb62.whdh
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17_ext3-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.grx4.smh9
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17_ext4-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.uqyp.t8kt
title: /SPS_D0ToKPi_JPsiPt-100To150_TuneCP5_13TeV-helaconia-pythia8-evtgen/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.hwyh.x4a9
title: /SPSToPsi2sJPsiTo4Mu2Pi_4Mu_Filter_TuneCP5_13TeV-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qi6o.z9lf
title: /SPSToPsi2sJPsiTo4Mu2Pi_4Mu_Filter_TuneCP5_13TeV-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.k6ag.rhb3
title: /SPSToPsi2sJPsiTo4Mu_4Mu_Filter_TuneCP5_13TeV-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.c4z2.oe63
title: /SPSToPsi2sJPsiTo4Mu_4Mu_Filter_TuneCP5_13TeV-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.jjk8.0szz
title: /SPS_ToY1SZ_Y1SToMuMu_ZToMuMu_TuneCP5_13TeV-helaconia-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v4/MINIAODSIM
DOI: 10.7483/opendata.cms.s6lk.9xzi
title: /SPS_ToY1SZ_Y1SToMuMu_ZToMuMu_TuneCP5_13TeV-helaconia-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v4/NANOAODSIM
DOI: 10.7483/opendata.cms.wbd5.t758
title: /SPS_UpsilonJPsi_TuneCP5_13TeV-JHUGen-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.rz6j.pllh
title: /SPS_UpsilonJPsi_TuneCP5_13TeV-JHUGen-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.m9ie.ysfq
title: /SPS_UpsilonJPsi_TuneCP5_v2_13TeV-JHUGen-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.m64z.0kvi
title: /SPS_UpsilonJPsi_TuneCP5_v2_13TeV-JHUGen-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.a7n2.egwm
title: /SSWW_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.94dm.rdmg
title: /SSWW_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tv3q.jo7q
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN10000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4dyr.37py
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN10000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5muk.tha5
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.aq7z.k8gu
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.g6l6.z3xy
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4c2a.d81r
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.az70.m94i
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.7uzx.sfff
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.puap.4fp1
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.5281/zenodo.7969743
title: FABLE Scenathon database 2021

description: This database contains key parameters and variables from the 2021 Scenathon run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modeling approach with multiple models developed by different teams in the world at national and regional scales and a methodology to link them, ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

The Scenathon 2021 database includes results at the global, country, and rest of the world region levels for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and agricultural input use. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2021 database:



Pathways: The Current Trends (CT) pathway reflects a low-ambition future shaped by existing policies.  The Sustainable pathway identifies additional actions to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Finland, Germany, India, Indonesia, Malaysia, Mexico, Norway, Russia, Rwanda, Sweden, South Africa, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050. Results are provided for each five-year step.

Trade adjustment: results are provided before and after the trade adjustment; the total imports are balanced.


The readme worksheet provides all the relevant information on the indicators and definitions of acronyms used in the database.
DOI: 10.5281/zenodo.7969744
title: FABLE Scenathon database 2021

description: This database contains key parameters and variables from the 2021 Scenathon run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modeling approach with multiple models developed by different teams in the world at national and regional scales and a methodology to link them, ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

The Scenathon 2021 database includes results at the global, country, and rest of the world region levels for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and agricultural input use. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2021 database:



Pathways: The Current Trends (CT) pathway reflects a low-ambition future shaped by existing policies.  The Sustainable pathway identifies additional actions to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Finland, Germany, India, Indonesia, Malaysia, Mexico, Norway, Russia, Rwanda, Sweden, South Africa, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050. Results are provided for each five-year step.

Trade adjustment: results are provided before and after the trade adjustment; the total imports are balanced.


The readme worksheet provides all the relevant information on the indicators and definitions of acronyms used in the database.
DOI: 10.7483/opendata.cms.jmnc.zelz
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.7kn1.ecro
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.06ns.2yf1
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.0jyn.35fr
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN15000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ycic.0pzd
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN15000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.6084/m9.figshare.22116371
title: Light conditions and the evolution of the visual system in birds

description: Materials contain R codes with analyses (*.R), phylogenetic trees (*.tre) and dataset (SupplementaryDataset1.xlsx). Dataset contains variables expressing visual system architecture and exposure to environmental light across birds:
-eye axial length
-cornreal diameter
-optic tectum volume
-visual wulst volume
-nocturnal habits
-prefered habitat
These variables were extracted from literature and open sources for 760 avian species.
DOI: 10.7483/opendata.cms.jv6j.c8an
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.6084/m9.figshare.22116371.v2
title: Light conditions and the evolution of the visual system in birds

description: Materials contain R codes with analyses (*.R), phylogenetic trees (*.tre) and dataset (SupplementaryDataset1.xlsx). Dataset contains variables expressing visual system architecture and exposure to environmental light across birds:
-eye axial length
-cornreal diameter
-optic tectum volume
-visual wulst volume
-nocturnal habits
-prefered habitat
These variables were extracted from literature and open sources for 760 avian species.
DOI: 10.7483/opendata.cms.we73.aowv
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ap2v.aww8
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.ov0x.5vvi
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.sxqt.fbdr
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.6yv5.f8kb
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN1750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.9eia.vdcw
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN20000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.a75y.qx8a
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN20000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.zx5u.buwk
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.8kxw.ewjb
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.8f25.yd6i
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xalk.6py3
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yth3.52dj
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.buqp.dejf
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.2q2q.5nto
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN5000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.csnm.e7mo
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN5000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.12t1.vmoi
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.7eh4.7qwl
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.cdlu.pwv6
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.3nnz.n46w
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.6h1p.1ifm
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.a5rl.aac7
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.horu.1ird
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN7500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.jwef.rk26
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN7500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.2e7n.bt0t
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.5281/zenodo.7969716
title: FABLE Scenathon database 2020

description: This database contains key parameters and variables from the 2020 Scenathon run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modeling approach with multiple models developed by different teams in the world at national and regional scales and a methodology to link them, ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

The Scenathon 2020 database includes results at the global, country, and rest of the world region levels for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and agricultural input use. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2020 database:



Pathways: The Current Trends (CT) pathway reflects a low-ambition future shaped by existing policies. The Sustainable pathway identifies additional actions necessary to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Finland, Germany, India, Indonesia, Malaysia, Mexico, Norway, Russia, Rwanda, Sweden, South Africa, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050, results are provided for each five-year-time step.

Trade adjustment: results are provided before and after the trade adjustment; the total imports are balanced.


The readme worksheet provides all the relevant information on the indicators and definitions of acronyms used in the database.
DOI: 10.5281/zenodo.7969717
title: FABLE Scenathon database 2020

description: This database contains key parameters and variables from the 2020 Scenathon run by the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) Consortium. A scenathon - a scenario marathon - is a multi-objective challenge that allows a decentralized global modeling approach with multiple models developed by different teams in the world at national and regional scales and a methodology to link them, ensuring international trade consistency and tracking collective progress towards the achievement of global sustainability targets. 

The Scenathon 2020 database includes results at the global, country, and rest of the world region levels for indicators related to food and nutrition security, land and biodiversity, GHG emissions from agriculture and land use change, and agricultural input use. It also includes key parameters that can be used to explain the results, such as the evolution of productivity and all supply and use balance items at the commodity level. It is possible to visualise some of the key results on the Scenathon dashboard.

Scope of the 2020 database:



Pathways: The Current Trends (CT) pathway reflects a low-ambition future shaped by existing policies. The Sustainable pathway identifies additional actions necessary to align national and regional pathways with global sustainability targets.

Countries and regions: Argentina, Australia, Brazil, Canada, China, Colombia, Ethiopia, Finland, Germany, India, Indonesia, Malaysia, Mexico, Norway, Russia, Rwanda, Sweden, South Africa, the UK, and the United States and the rest of the world regions Rest of Asia and Pacific, Rest of Central and South America, Rest of European Union, Rest of Europe non-EU, Rest of Sub-Saharan Africa.

Time: 2000-2050, results are provided for each five-year-time step.

Trade adjustment: results are provided before and after the trade adjustment; the total imports are balanced.


The readme worksheet provides all the relevant information on the indicators and definitions of acronyms used in the database.
DOI: 10.7483/opendata.cms.dl9x.gvyv
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.51ih.mdy0
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.w0sl.epg7
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.nnh2.j6j3
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.mbzy.ctvj
title: /SSWWTypeIHeavyN-DFmix_Dilepton_NLO_MN900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.14651010
title: BADIIY-TARIXIY MATERIALLAR VOSITASIDA  BO'LAJAK O'QITUVCHILARNI MA'NAVIY MADANIYATINI RIVOJLANTIRISHNING AHAMIYATI

description: Zamonaviy ta’lim tizimida ma’naviyat, insoniyatning ruhiy va axloqiy o‘sishi uchun eng muhim omillardan biri sifatida qaraladi. Xususan, o‘qituvchilarning ma’naviy madaniyati, nafaqat ularning shaxsiy rivojlanishi, balki ularning jamoaviy hayotini shakllantirishda ham muhim rol o‘ynaydi.  Ushbu maqolada badiiy-tarixiy materiallar yordamida bo‘lajak o‘qituvchilarning ma’naviy madaniyatini rivojlantirishning ahamiyati, asosiy tamoyillari hamda eng samarali usullari haqida so‘z yuritiladi.