cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13937894
title: COVID-19-Hospitalisierungen in Deutschland

description: Im Datensatz 'COVID-19-Hospitalisierungen' werden die aktuellen Zahlen der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - erfassten hospitalisierten COVID-19-Fälle bereitgestellt.
Um den Trend der Anzahl von Hospitalisierungen und der 7-Tage-Hospitalisierungsinzidenz besser bewerten zu können, wird die berichtete Hospitalisierungsinzidenz um eine Schätzung der zu erwartenden Anzahl an verzögert berichteten Hospitalisierungen ergänzt. Neben den Daten der gemeldeten COVID-19-Hospitalisierungen auf Bundes- und Länderebene wird daher ein Nowcasting der Anzahl hospitalisierter Fälle und der 7-Tage-Hospitalisierungsinzidenz auf Bundesebene durchgeführt. Ziel ist die Schätzung der Anzahl von hospitalisierten COVID-19-Fällen mit Meldedatum innerhalb der sieben vorhergehenden Tage - inklusive der noch nicht an das RKI berichteten Hospitalisierungen. Aufbauend auf dem Nowcasting wird eine Schätzung der adjustierten 7-Tage-Hospitalisierungsinzidenz durchgeführt.
DOI: 10.5281/zenodo.13979896
title: COVID-19-Hospitalisierungen in Deutschland

description: Im Datensatz 'COVID-19-Hospitalisierungen' werden die aktuellen Zahlen der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - erfassten hospitalisierten COVID-19-Fälle bereitgestellt.
Um den Trend der Anzahl von Hospitalisierungen und der 7-Tage-Hospitalisierungsinzidenz besser bewerten zu können, wird die berichtete Hospitalisierungsinzidenz um eine Schätzung der zu erwartenden Anzahl an verzögert berichteten Hospitalisierungen ergänzt. Neben den Daten der gemeldeten COVID-19-Hospitalisierungen auf Bundes- und Länderebene wird daher ein Nowcasting der Anzahl hospitalisierter Fälle und der 7-Tage-Hospitalisierungsinzidenz auf Bundesebene durchgeführt. Ziel ist die Schätzung der Anzahl von hospitalisierten COVID-19-Fällen mit Meldedatum innerhalb der sieben vorhergehenden Tage - inklusive der noch nicht an das RKI berichteten Hospitalisierungen. Aufbauend auf dem Nowcasting wird eine Schätzung der adjustierten 7-Tage-Hospitalisierungsinzidenz durchgeführt.
DOI: 10.6084/m9.figshare.27934303
title: TMS in the Kingdom of Denmark: an overview of current clinical practice

description: Repetitive TMS (rTMS) has been demonstrated to be an effective treatment of several neuropsychiatric disorders. Its safety and efficacy are well established, and multiple rTMS devices have been approved by both Conformitè Europëenne Mark and U.S. Food and Drug Administration. We aimed to survey TMS practice in Psychiatry in the Kingdom of Denmark and compare it with the international state of the art. A survey of rTMS clinical practice in 2023 was sent to all general adult psychiatry departments practicing TMS in the Danish Realm (Denmark = 10, Faroe Islands = 0, Greenland = 0). Response rate was 100%. rTMS was available in 37% of psychiatric departments and 3 out of 5 Danish Regions. Admission criteria required a diagnosis of unipolar depression with a degree of treatment-resistance or unacceptable side-effects to antidepressant treatment. Common contraindications included: cochlear implants (100%), pacemaker and neurostimulators (80%), other ferromagnetic/implanted devices in head, neck, or thorax (70%), active substance misuse (60%), and electrolytic disturbances (50%). Three rTMS protocols were identified: 10 Hz rTMS delivered over the L-DLPFC, iTBS delivered over the L-DLPFC and 1 Hz rTMS delivered over the right-DLPFC. 383 patients were treated with TMS. rTMS is unequally available in the public healthcare of the Kingdom of Denmark. Existing strategies for solving inequalities could address such issues. Unipolar depression was the only psychiatric disorder treated with rTMS in 2023. rTMS practice in the Danish Realm considers the use of evidence-based protocols and is consistent with recommendations from international expert guidelines.
DOI: 10.6084/m9.figshare.27934303.v1
title: TMS in the Kingdom of Denmark: an overview of current clinical practice

description: Repetitive TMS (rTMS) has been demonstrated to be an effective treatment of several neuropsychiatric disorders. Its safety and efficacy are well established, and multiple rTMS devices have been approved by both Conformitè Europëenne Mark and U.S. Food and Drug Administration. We aimed to survey TMS practice in Psychiatry in the Kingdom of Denmark and compare it with the international state of the art. A survey of rTMS clinical practice in 2023 was sent to all general adult psychiatry departments practicing TMS in the Danish Realm (Denmark = 10, Faroe Islands = 0, Greenland = 0). Response rate was 100%. rTMS was available in 37% of psychiatric departments and 3 out of 5 Danish Regions. Admission criteria required a diagnosis of unipolar depression with a degree of treatment-resistance or unacceptable side-effects to antidepressant treatment. Common contraindications included: cochlear implants (100%), pacemaker and neurostimulators (80%), other ferromagnetic/implanted devices in head, neck, or thorax (70%), active substance misuse (60%), and electrolytic disturbances (50%). Three rTMS protocols were identified: 10 Hz rTMS delivered over the L-DLPFC, iTBS delivered over the L-DLPFC and 1 Hz rTMS delivered over the right-DLPFC. 383 patients were treated with TMS. rTMS is unequally available in the public healthcare of the Kingdom of Denmark. Existing strategies for solving inequalities could address such issues. Unipolar depression was the only psychiatric disorder treated with rTMS in 2023. rTMS practice in the Danish Realm considers the use of evidence-based protocols and is consistent with recommendations from international expert guidelines.
DOI: 10.6084/m9.figshare.28103373
title: Gender-stratified national mental health norms of BSI-53, BSI-18, SCL-10, ADHD-9, and ADHD-6 for Denmark

description: Reliable gender-sensitive normative data is needed to facilitate mental health research and clinical utility of commonly used symptoms scales. This study establishes Danish gender-stratified norms for the 53-item and 18-item Brief Symptom Inventory (BSI-53, BSI-18), proposed attention deficit hyperactivity disorder (ADHD) symptomatology scales from the BSI-53, and the 10-item Symptom Checklist (SCL-10). This study also examines gender-differences in symptom reporting of the ADHD and SCL-10 scales, and assesses potential bias in recent SCL-10 norms. Norms for the BSI-53, BSI-18, two ADHD scales and SCL-10 were established using archived Hopkins Symptom Checklist 90-R (SCL-90R) normative data collected in spring 2000 (<i>n</i> = 1079, age 18–80, 53% female). SCL-10 norms from 2000 were compared with proposed norms collected in spring 2020 (<i>n</i> = 2819, age 18–80, 55% female) during the first COVID-19 lockdown in Denmark, and with first-episode depression patients from 2020 to 2023 (<i>n</i> = 221, age 18–65, 73% female). The scales showed high internal consistencies (McDonald’s <i>ω</i> &gt; 0.81), except for 4–5 item scales (<i>ω</i> &gt; 0.73). Women scored higher on both ADHD scales (Cohen’s <i>d</i> = 0.15 and 0.19, <i>p</i> ≤ 0.019). Women scored higher on the SCL-10 in the 2000 population sample (<i>d</i> = 0.22, <i>p</i> &lt; 0.0001) and in patients (<i>d</i> = 0.44, <i>p</i> = 0.004). The average population SCL-10 score in 2000 was lower than 2020 (<i>d</i> = 0.52, <i>p</i> &lt; 0.001). This study provides gender-stratified Danish mental health norms for multiple symptom scales. The considerable gender differences in the SCL-10 underscore the importance of gender-specific norms. The 2020 SCL-10 norms are biased by COVID-19 distress. Until new normative data is available, the gender-specific norms provided here are recommended.
DOI: 10.6084/m9.figshare.28103373.v1
title: Gender-stratified national mental health norms of BSI-53, BSI-18, SCL-10, ADHD-9, and ADHD-6 for Denmark

description: Reliable gender-sensitive normative data is needed to facilitate mental health research and clinical utility of commonly used symptoms scales. This study establishes Danish gender-stratified norms for the 53-item and 18-item Brief Symptom Inventory (BSI-53, BSI-18), proposed attention deficit hyperactivity disorder (ADHD) symptomatology scales from the BSI-53, and the 10-item Symptom Checklist (SCL-10). This study also examines gender-differences in symptom reporting of the ADHD and SCL-10 scales, and assesses potential bias in recent SCL-10 norms. Norms for the BSI-53, BSI-18, two ADHD scales and SCL-10 were established using archived Hopkins Symptom Checklist 90-R (SCL-90R) normative data collected in spring 2000 (<i>n</i> = 1079, age 18–80, 53% female). SCL-10 norms from 2000 were compared with proposed norms collected in spring 2020 (<i>n</i> = 2819, age 18–80, 55% female) during the first COVID-19 lockdown in Denmark, and with first-episode depression patients from 2020 to 2023 (<i>n</i> = 221, age 18–65, 73% female). The scales showed high internal consistencies (McDonald’s <i>ω</i> &gt; 0.81), except for 4–5 item scales (<i>ω</i> &gt; 0.73). Women scored higher on both ADHD scales (Cohen’s <i>d</i> = 0.15 and 0.19, <i>p</i> ≤ 0.019). Women scored higher on the SCL-10 in the 2000 population sample (<i>d</i> = 0.22, <i>p</i> &lt; 0.0001) and in patients (<i>d</i> = 0.44, <i>p</i> = 0.004). The average population SCL-10 score in 2000 was lower than 2020 (<i>d</i> = 0.52, <i>p</i> &lt; 0.001). This study provides gender-stratified Danish mental health norms for multiple symptom scales. The considerable gender differences in the SCL-10 underscore the importance of gender-specific norms. The 2020 SCL-10 norms are biased by COVID-19 distress. Until new normative data is available, the gender-specific norms provided here are recommended.
DOI: 10.15468/dl.e4tm2g
title: Occurrence Download

description: A dataset containing 3039 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Phenology and biological traits of migrating river lamprey sampled by electrofishing in the Oir river (France)."
 ]
}

 The dataset includes 3039 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070171-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z4wkar
title: Occurrence Download

description: A dataset containing 956900 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Plecoptera"
 ]
}

 The dataset includes 956900 records from 870 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070164-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.6964944
title: SAP Signavio Academic Models

description: The SAP Signavio Academic Models (SAP-SAM) dataset contains hundreds of thousands of business process models (as well as other business models, such as business decision models and value chains) that have been created over the course of more than a decade by the SAP Signavio BPM Academic Initiative. Detailed instructions on how to work with the dataset, as well as example scripts, are available on GitHub. A paper (pre-print) that describes the dataset is available on the arXiv.

Cite

For citing the paper about the dataset, use the following Bibtex entry (the Bibtex entry for citing the dataset itself is provided by Zenodo):

@inproceedings{DBLP:conf/icpm/SolaWSBRK22,  author       = {Diana Sola and                  Christian Warmuth and                  Bernhard Sch{\"{a}}fer and                  Peyman Badakhshan and                  Jana{-}Rebecca Rehse and                  Timotheus Kampik},  editor       = {Marco Montali and                  Arik Senderovich and                  Matthias Weidlich},  title        = {{SAP} Signavio Academic Models: {A} Large Process Model Dataset},  booktitle    = {Process Mining Workshops - {ICPM} 2022 International Workshops, Bozen-Bolzano,                  Italy, October 23-28, 2022, Revised Selected Papers},  series       = {Lecture Notes in Business Information Processing},  volume       = {468},  pages        = {453--465},  publisher    = {Springer},  year         = {2022},  url          = {https://doi.org/10.1007/978-3-031-27815-0\_33},  doi          = {10.1007/978-3-031-27815-0\_33}}

 

LICENSE

Copyright (c) 2022 by SAP. 

SAP grants to Recipient a non-exclusive copyright license to the Model Collection to use the Model Collection for Non-Commercial Research purposes of evaluating Recipient’s algorithms or other academic research artefacts against the Model Collection. Any rights not explicitly granted herein are reserved to SAP. For the avoidance of doubt, no rights to make derivative works of the Model Collection is granted and the license granted hereunder is for Non-Commercial Research purposes only. 

“Model Collection” shall mean all files in this archive (which are JSON, XML, or other representation of business process models or other models). 

“Recipient” means any natural person receiving the Model Collection. 

“Non-Commercial Research” means research solely for the advancement of knowledge whether by a university or other learning institution and does not include any commercial or other sales objectives.
DOI: 10.5281/zenodo.6964945
title: SAP Signavio Academic Models

description: The SAP Signavio Academic Models (SAP-SAM) dataset contains hundreds of thousands of business process models (as well as other business models, such as business decision models and value chains) that have been created over the course of more than a decade by the SAP Signavio BPM Academic Initiative. Detailed instructions on how to work with the dataset, as well as example scripts, are available on GitHub. <strong>LICENSE</strong> Copyright (c) 2022 by SAP. SAP grants to Recipient a non-exclusive copyright license to the Model Collection to use the Model Collection for Non-Commercial Research purposes of evaluating Recipient’s algorithms or other academic research artefacts against the Model Collection. Any rights not explicitly granted herein are reserved to SAP. For the avoidance of doubt, no rights to make derivative works of the Model Collection is granted and the license granted hereunder is for Non-Commercial Research purposes only. “Model Collection” shall mean all files in this archive (which are JSON, XML, or other representation of business process models or other models). “Recipient” means any natural person receiving the Model Collection. “Non-Commercial Research” means research solely for the advancement of knowledge whether by a university or other learning institution and does not include any commercial or other sales objectives.
DOI: 10.5281/zenodo.7012043
title: SAP Signavio Academic Models

description: The SAP Signavio Academic Models (SAP-SAM) dataset contains hundreds of thousands of business process models (as well as other business models, such as business decision models and value chains) that have been created over the course of more than a decade by the SAP Signavio BPM Academic Initiative. Detailed instructions on how to work with the dataset, as well as example scripts, are available on GitHub. A paper (pre-print) that describes the dataset is available on the arXiv.

Cite

For citing the paper about the dataset, use the following Bibtex entry (the Bibtex entry for citing the dataset itself is provided by Zenodo):

@inproceedings{DBLP:conf/icpm/SolaWSBRK22,  author       = {Diana Sola and                  Christian Warmuth and                  Bernhard Sch{\"{a}}fer and                  Peyman Badakhshan and                  Jana{-}Rebecca Rehse and                  Timotheus Kampik},  editor       = {Marco Montali and                  Arik Senderovich and                  Matthias Weidlich},  title        = {{SAP} Signavio Academic Models: {A} Large Process Model Dataset},  booktitle    = {Process Mining Workshops - {ICPM} 2022 International Workshops, Bozen-Bolzano,                  Italy, October 23-28, 2022, Revised Selected Papers},  series       = {Lecture Notes in Business Information Processing},  volume       = {468},  pages        = {453--465},  publisher    = {Springer},  year         = {2022},  url          = {https://doi.org/10.1007/978-3-031-27815-0\_33},  doi          = {10.1007/978-3-031-27815-0\_33}}

 

LICENSE

Copyright (c) 2022 by SAP. 

SAP grants to Recipient a non-exclusive copyright license to the Model Collection to use the Model Collection for Non-Commercial Research purposes of evaluating Recipient’s algorithms or other academic research artefacts against the Model Collection. Any rights not explicitly granted herein are reserved to SAP. For the avoidance of doubt, no rights to make derivative works of the Model Collection is granted and the license granted hereunder is for Non-Commercial Research purposes only. 

“Model Collection” shall mean all files in this archive (which are JSON, XML, or other representation of business process models or other models). 

“Recipient” means any natural person receiving the Model Collection. 

“Non-Commercial Research” means research solely for the advancement of knowledge whether by a university or other learning institution and does not include any commercial or other sales objectives.
DOI: 10.5281/zenodo.14649852
title: Annotated dataset_microfauna
DOI: 10.5281/zenodo.14649853
title: Annotated dataset_microfauna
DOI: 10.5281/zenodo.14650021
title: New insights into sulfuric acid speleogenesis in the Plavecký Karst, Slovakia: A conceptual model of geochemical sources.

description: The XRD data used in the paper New Insights into Sulfuric Acid Speleogenesis in the Plavecký Karst, Slovakia: A Conceptual Model of Geochemical Sources.
DOI: 10.5281/zenodo.14650022
title: New insights into sulfuric acid speleogenesis in the Plavecký Karst, Slovakia: A conceptual model of geochemical sources.

description: The XRD data used in the paper New Insights into Sulfuric Acid Speleogenesis in the Plavecký Karst, Slovakia: A Conceptual Model of Geochemical Sources.
DOI: 10.15468/dl.759wdd
title: Occurrence Download

description: A dataset containing 5915 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Axis axis (Erxleben, 1777)"
 ]
}

 The dataset includes 5915 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070176-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28088024.v1
title: Genome of root celery

description: The genome of the celeriac (Apium graveolens var. rapaceum) cultivar Alabaster (PI 662464).
DOI: 10.15468/dl.ytekay
title: Occurrence Download

description: A dataset containing 1097 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Nageia nagi (Thunb.) Kuntze"
 ]
}

 The dataset includes 1097 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070178-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bu36tx
title: Occurrence Download

description: A dataset containing 125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((19.20999 -33.86803,19.27504 -33.86683,19.27615 -33.81519,19.21116 -33.81645,19.20999 -33.86803))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 125 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070183-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djk46x
title: Occurrence Download

description: A dataset containing 6544 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quercus variabilis Blume",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6544 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070184-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wzes8u
title: Occurrence Download

description: A dataset containing 81424 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Aythya fuligula (Linnaeus, 1758)",
 "Year 2010-2010"
 ]
}

 The dataset includes 81424 records from 181 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070180-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3rtezu
title: Occurrence Download

description: A dataset containing 1653 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Fejervarya multistriata (Hallowell, 1861)"
 ]
}

 The dataset includes 1653 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070186-241126133413365/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.j8w4.x06b
title: /NMSSM_XToYHTo6B_MX-1800_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qhq5.eeza
title: /NMSSM_XToYHTo6B_MX-1800_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.7561.iscz
title: /NMSSM_XToYHTo6B_MX-1800_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.suvu.sjb7
title: /NMSSM_XToYHTo6B_MX-1800_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.jsin.ipil
title: /NMSSM_XToYHTo6B_MX-1800_MY-350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4aia.16il
title: /NMSSM_XToYHTo6B_MX-1800_MY-400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.lw5t.5kky
title: /NMSSM_XToYHTo6B_MX-1800_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.526i.l4ew
title: /NMSSM_XToYHTo6B_MX-1800_MY-450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.rvj8.osg2
title: /NMSSM_XToYHTo6B_MX-1800_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.t4t0.inoq
title: /NMSSM_XToYHTo6B_MX-1800_MY-500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.oi2i.nqwf
title: /NMSSM_XToYHTo6B_MX-1800_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.rcws.zhge
title: /NMSSM_XToYHTo6B_MX-1800_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yrta.bqy5
title: /NMSSM_XToYHTo6B_MX-1800_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.d09p.20jz
title: /NMSSM_XToYHTo6B_MX-1800_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.opm7.ekm0
title: /NMSSM_XToYHTo6B_MX-1800_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM