cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/xp5s77y7zj.2
title: Dataset of "Evaluation of leg symmetry in muscle oxygen saturation during submaximal to maximal cycling exercise"

description: Dataset of the manuscript tilted "Evaluation of leg symmetry in muscle oxygen saturation during submaximal to maximal cycling exercise". In this work we determined whether oxygenation profiles in the three active muscles of both legs were symmetrical during cycling at different intensities. Twenty-six trained cyclists and triathletes completed a graded exercise test (GXT) and an 8-minute Functional Threshold Power estimation test (8MTT) on a cycle ergometer over two non-consecutive days. SmO2 was bilaterally assessed using NIRS technology in the vastus lateralis, gastrocnemius medialis, and tibialis anterior.
DOI: 10.17632/j84ssttmp8.1
title: socio-economic, educational, agricultural, and infrastructure indicators for regional analysis

description: The dataset is organized into four broad categories: socio-economic, education, agriculture and infrastructure, allowing a comprehensive and cross-sectional analysis of regional dynamics in Morocco. Each category includes key indicators, measured at the regional level, with clearly defined units of measurement.
DOI: 10.17632/4b65hzcpys.1
title: Comprehensive national-level technology adoption dataset (1720-2022)

description: Dataset Summary: 

This collection presents a comprehensive dataset on global technology adoption and diffusion, meticulously compiled to enhance the understanding of technological spread over time and across different regions. It includes detailed records of technology adoption levels alongside extensive metadata, making it an invaluable resource for a wide range of analyses including technological impact assessment, diffusion pattern studies, and sector-specific technological advancements. The dataset spans 292 territories from 1720-2022.

Data Files:

TechnologyDataFull.csv: Captures technology adoption across various countries, organized in a long format representing different technologies. The dataset extends from historical to recent times allowing for an analysis of trends and patterns in technology uptake. This dataset is key for longitudinal studies of technology adoption.

TechnologyMetadata.csv: Complements the adoption data by providing contextual and detailed descriptive information on each technology. Key details include a common name (Tech_Name), a more detailed variant (Detailed_Tech_Name), sector affiliation, and the invention details (year and country), along with a unique identifier (Variable_ID) for each technology. 

Technology_Data_Description.pdf: A descriptive document that provides an exhaustive overview of the datasets’ contents and structure. It details the data sources, the integration and normalization methods used to compile the datasets, and discusses the methodologies employed to ensure data accuracy and relevance.

Key Features:

- Detailed technological adoption data across a diverse array of global regions.
- Rich metadata that supports deeper analytical pursuits into technology diffusion.
- Documentation that outlines the datasets' compilation from 32 distinct international technology tracking initiatives for enhanced spatial and temporal coverage.

This dataset is designed to address spatial and temporal deficiencies found in previous compilations, thereby providing a more accurate representation of global technology distribution. It is suitable for researchers interested in the dynamics of technology diffusion, policy-making in technology governance, and strategic planning in technology development and deployment.
DOI: 10.7483/opendata.cms.d4et.e2k2
title: /TstarTstarToTgluonTgluon_M-1800_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.7su6.q9bi
title: /TstarTstarToTgluonTgluon_M-1900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.yjwo.8ie1
title: /TstarTstarToTgluonTgluon_M-1900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.ex6bqk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lacon graecus (Candèze, 1857)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071243-241126133413365/datasets/export for details.
DOI: 10.7483/opendata.cms.dauj.bjyq
title: /TstarTstarToTgluonTgluon_M-1900_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/r76v5sjyv2.2
title: Semantic Role Labeling Datasets for Crisis Event

description: This dataset is a collection of Indonesian language text data extracted from Twitter and designed to support research in Semantic Role Labeling (SRL) and Named Entity Recognition (NER) tasks. This dataset offers a combination of argument labels for SRL and entity labels for NER, which are very important in information extraction. Through SRL, the dataset can identify semantic roles, such as who the victim is, where the location is, what the cause is, and how the impact of the crisis event is. Meanwhile, NER can recognize entities such as the name of a place or organization involved.

The data was collected using the Twitter API with relevant keywords from January 2018 to December 2023, covering four major crisis events in Indonesia: fires, accidents, floods, and earthquakes. Two experienced annotators carried out the annotation process—a disaster management expert and a doctoral student in computer science. The level of agreement between annotators was measured using Fleiss' Kappa, with results above 0.80 for all labels, indicating high data quality and consistency in labeling.
DOI: 10.7483/opendata.cms.xhgg.sirb
title: /TstarTstarToTgluonTgluon_M-1900_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/tf9nx8xhcm.2
title: How do substrate composition and environmental variables influence the richness and abundance of molluscs in an urban river from the Brazilian semiarid region?

description: This study investigates the influence of substrate composition and environmental variables on the richness and abundance of freshwater molluscs in the urban stretch of the Guaribas River, located in the semi-arid region of Piauí, Brazil. Bimonthly collections were conducted between October 2021 and August 2022 across five sampling points, where molluscs were gathered from sediment and macrophyte substrates. Environmental parameters, including pH, water temperature, and water column depth, were also recorded. A total of 2479 individuals were identified, predominantly gastropods, with higher species richness and abundance observed in macrophyte substrates compared to sediment. Statistical analysis revealed a positive correlation between mollusc abundance and macrophyte presence and a significant interaction between pH and temperature on mollusc distribution. The results highlight the crucial role of macrophytes in enhancing habitat complexity and supporting mollusc diversity while demonstrating the negative impact of sediment pollutants in urban river systems. These findings provide insights into the ecological dynamics of molluscs in semi-arid environments, contributing to conservation and management strategies in these regions.
DOI: 10.7483/opendata.cms.6emn.fh11
title: /TstarTstarToTgluonTgluon_M-2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/3wcm3zmvmg.1
title: Dados Brutos do Questionário dos Sintomas

description: Dados Brutos do Questionário dos Sintomas
DOI: 10.17632/5fbby4w4z3.1
title: Dados Brutos do Questionário Quality of Recovery 40 items

description: Dados Brutos do Questionário Quality of Recovery 40 items
DOI: 10.7483/opendata.cms.g90k.bx34
title: /TstarTstarToTgluonTgluon_M-2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/9nm72wptnv.1
title: Comprehensive plasma cytokine profiling reveals a distinct endotype of Type 2 Inflammation in Chronic Pruritus of Unknown Origin

description: Supplemental data for "Comprehensive plasma cytokine profiling reveals a distinct endotype of Type 2 Inflammation in Chronic Pruritus of Unknown Origin"
DOI: 10.7483/opendata.cms.kwnk.acom
title: /TstarTstarToTgluonTgluon_M-2000_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/vbtx56wgyw.1
title: Supplementary Files: Identification of Quantitative Trait Loci for Body Conformation and Head Shape in the American Quarter Horse

description: Supplemental material available
DOI: 10.17632/t78cvr25ty.1
title: Data for the study of the impact of the production of Colombian Certified Coffee on water

description: The data of 461 certified and non-certified coffee farms are presented with the values of the water quality indices according to the farm's domestic wastewater treatment system, the coffee processing system, and the management of the by-products of the wet milling process.
DOI: 10.17632/yg58dn8b9c.1
title: AI-Solubility algorithm

description: The model development was carried out in the Google Colaboratory environment, using the NumPy (version 1.25.2), Pandas (version 2.0.3) and Matplotlib (version 3.7.1) libraries for data processing and plotting. The 4615 available data were split into 80% training data and 20% test data using the train_test_split function of the scikit-learn library with a random seed of random_state=0. The data were then normalised. 
The data was then normalised using the StandardScaler method. A neural network was implemented with an input layer of 66 neurons, a hidden layer of 128 neurons and a ReLu activation function, and an output layer of one neuron and a ReLu activation function to predict solubility.
The neural network was trained using the Adam optimiser with a learning rate (learning_rate=0.001) of 35 epochs using the Keras library of TensorFlow. To avoid overfitting, Dropout, with a dropout rate of 20% was used as a regularisation technique by deactivating neurons during training to avoid overfitting. The different accuracy metrics were evaluated to analyse the model performance.
DOI: 10.7483/opendata.cms.kjr9.leou
title: /TstarTstarToTgluonTgluon_M-2000_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/yg58dn8b9c.2
title: AI-Solubility algorithm

description: The model development was carried out in the Google Colaboratory environment, using the NumPy (version 1.25.2), Pandas (version 2.0.3) and Matplotlib (version 3.7.1) libraries for data processing and plotting. The 4615 available data were split into 80% training data and 20% test data using the train_test_split function of the scikit-learn library with a random seed of random_state=0. The data were then normalised. 
The data was then normalised using the StandardScaler method. A neural network was implemented with an input layer of 66 neurons, a hidden layer of 128 neurons and a ReLu activation function, and an output layer of one neuron and a ReLu activation function to predict solubility.
The neural network was trained using the Adam optimiser with a learning rate (learning_rate=0.001) of 35 epochs using the Keras library of TensorFlow. To avoid overfitting, Dropout, with a dropout rate of 20% was used as a regularisation technique by deactivating neurons during training to avoid overfitting. The different accuracy metrics were evaluated to analyse the model performance.
DOI: 10.7483/opendata.cms.u2mf.qalw
title: /TstarTstarToTgluonTgluon_M-2250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.17632/xw7v5fgrrv.1
title: ECU_Data

description: Field Experiment Data - Airline Itineraries booked via airlines web sites and online travel agencies
DOI: 10.7483/opendata.cms.cxqz.nf52
title: /TstarTstarToTgluonTgluon_M-2250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.yvbo.1nss
title: /TstarTstarToTgluonTgluon_M-2250_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.xqfg.4k03
title: /TstarTstarToTgluonTgluon_M-2250_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.d9i1.669r
title: /TstarTstarToTgluonTgluon_M-2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.y8gu.f540
title: /TstarTstarToTgluonTgluon_M-2500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.nqpx.o6qg
title: /TstarTstarToTgluonTgluon_M-2500_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.6nne.uqa2
title: /TstarTstarToTgluonTgluon_M-2500_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5auf.21rt
title: /TstarTstarToTgluonTgluon_M-2750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.if20.vuxe
title: /TstarTstarToTgluonTgluon_M-2750_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.q3ew.0yez
title: /TstarTstarToTgluonTgluon_M-2750_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nj9x.xgw7
title: /TstarTstarToTgluonTgluon_M-2750_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fklc.u7w2
title: /TstarTstarToTgluonTgluon_M-3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.rpji.bmjw
title: /TstarTstarToTgluonTgluon_M-3000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.i6an.ni8v
title: /TstarTstarToTgluonTgluon_M-3000_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.38fh.nqom
title: /TstarTstarToTgluonTgluon_M-3000_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.66fmhs
title: Occurrence Download

description: A dataset containing 263307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Zenaida asiatica (Linnaeus, 1758)"
 ]
}

 The dataset includes 263307 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071185-241126133413365/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.efod.0yit
title: /TstarTstarToTgluonTgluon_M-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.zrsm.jyh1
title: /TstarTstarToTgluonTgluon_M-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.c4gy5d
title: Occurrence Download

description: A dataset containing 555 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lamprohiza mulsantii (Kiesenwetter, 1850)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 555 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071245-241126133413365/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.2yjk.fufb
title: /TstarTstarToTgluonTgluon_M-700_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.av3b.4i0l
title: /TstarTstarToTgluonTgluon_M-700_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.n8p0.50ly
title: /TstarTstarToTgluonTgluon_M-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.53j9.lcf5
title: /TstarTstarToTgluonTgluon_M-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.qh5w.vkzf
title: /TstarTstarToTgluonTgluon_M-800_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.ufeo.9t8l
title: /TstarTstarToTgluonTgluon_M-800_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5283/epub.54717
title: Data archive of "How general is the natural frequency effect? The case of joint probabilities"

description: Dataset - Joint Probabilities for the submitted article
DOI: 10.7483/opendata.cms.e6cd.y6ih
title: /TstarTstarToTgluonTgluon_M-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.p0pd.eswv
title: /TstarTstarToTgluonTgluon_M-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.dgev.et6s
title: /TstarTstarToTgluonTgluon_M-900_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.tfmf.dzje
title: /TstarTstarToTgluonTgluon_M-900_TuneCP5_Spin32_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.zo2h.s3z3
title: /VBFGamma_5f_TuneCP5_DipoleRecoil_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.d1k0.2iid
title: /VBFGamma_5f_TuneCP5_DipoleRecoil_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.ibm9.8qk1
title: /VBFHToBB_M-125_dipoleRecoilOn_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.oagh.6twj
title: /VBFHToBB_M-125_dipoleRecoilOn_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.2w1n.yhlk
title: /VBFHToBB_M-125_TuneCH3_13TeV-powheg-herwig/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v4/MINIAODSIM
DOI: 10.7483/opendata.cms.omx3.bs51
title: /VBFHToBB_M-125_TuneCH3_13TeV-powheg-herwig/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.csh9.dfb9
title: /VBFHToBB_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.il3e.ionr
title: /VBFHToBB_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.9ir1.lfsv
title: /VBFHToCC_M-125_dipoleRecoilOn_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.czxy.bvyz
title: /VBFHToCC_M-125_dipoleRecoilOn_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2/NANOAODSIM
DOI: 10.7483/opendata.cms.oyew.fh5c
title: /VBFHToCC_M-125_TuneCH3_13TeV-powheg-herwig/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.moxl.egj4
title: /VBFHToCC_M-125_TuneCH3_13TeV-powheg-herwig/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.lu6q.g5ai
title: /VBFHToCC_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.17632/yncw8whw7m.1
title: Code - An integrative multiparametric approach stratifies distinct phenotypes of blast phase chronic myelomonocytic leukemia with differential maturation status and drug sensitivity profiles

description: Approximately 30% of chronic myelomonocytic leukemia patients undergo transformation to a chemo-refractory blastic phase (BP-CMML). Seeking novel therapeutic approaches we profiled blast transcriptomes from 42 BP-CMMLs, observing extensive transcriptional heterogeneity and poor alignment to current AML classifications. BP-CMMLs displayed distinctive transcriptomic profiles, including enrichment for quiescence and variability in drug response signatures. Integrating clinical, immunophenotype and transcriptome parameters, RandomForest unsupervised clustering distinguished immature and mature subtypes characterized by differential expression of transcriptional modules, oncogenes, apoptotic regulators and patterns of surface marker expression. Subtypes differed in predicted response to AML drugs, validated ex vivo in primary samples. Iteratively refined stratification resolved a classification structure comprising five subtypes along a maturation spectrum, predictive of response to novel agents including consistent patterns for RTK, CDK, MTOR and MAPK inhibitors. Finally, we generated a clinically-applicable decision tree to stratify BP-CMML with high specificity and sensitivity, and potentially guide personalized drug selection for improved outcomes.

###################
# Code description: #
###################

Codes included within this data repository are to be used for figure reproduction or further exploration.
DOI: 10.17632/yncw8whw7m.2
title: Code - An integrative multiparametric approach stratifies distinct phenotypes of blast phase chronic myelomonocytic leukemia with differential maturation status and drug sensitivity profiles

description: Approximately 30% of chronic myelomonocytic leukemia patients undergo transformation to a chemo-refractory blastic phase (BP-CMML). Seeking novel therapeutic approaches we profiled blast transcriptomes from 42 BP-CMMLs, observing extensive transcriptional heterogeneity and poor alignment to current AML classifications. BP-CMMLs displayed distinctive transcriptomic profiles, including enrichment for quiescence and variability in drug response signatures. Integrating clinical, immunophenotype and transcriptome parameters, RandomForest unsupervised clustering distinguished immature and mature subtypes characterized by differential expression of transcriptional modules, oncogenes, apoptotic regulators and patterns of surface marker expression. Subtypes differed in predicted response to AML drugs, validated ex vivo in primary samples. Iteratively refined stratification resolved a classification structure comprising five subtypes along a maturation spectrum, predictive of response to novel agents including consistent patterns for RTK, CDK, MTOR and MAPK inhibitors. Finally, we generated a clinically-applicable decision tree to stratify BP-CMML with high specificity and sensitivity, and potentially guide personalized drug selection for improved outcomes.

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Codes included within this data repository are to be used for figure reproduction or further exploration.
DOI: 10.7483/opendata.cms.b83f.dkdm
title: /VBFHToCC_M-125_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ry81.ws8v
title: /VBFHToEEG_M120_Dalitz_TuneCP5_13TeV-amcatnlo-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v1/MINIAODSIM
DOI: 10.7483/opendata.cms.e5p4.9eue
title: /VBFHToEEG_M120_Dalitz_TuneCP5_13TeV-amcatnlo-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM