cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.14666127
title: DNA methylation signatures predict cytogenetic subtype and outcome in pediatric acute myeloid leukemia (AML)

description: This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.<br>For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.<br>
DOI: 10.7483/opendata.cms.m3de.ieww
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.14666127.v2
title: DNA methylation signatures predict cytogenetic subtype and outcome in pediatric acute myeloid leukemia (AML)

description: This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.<br>For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.<br>
DOI: 10.7483/opendata.cms.mpwo.pby7
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH25_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.14666127.v1
title: DNA methylation signatures predict cytogenetic subtype and outcome in pediatric acute myeloid leukemia (AML)

description: This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument). This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The raw idat files were processed with Methylation Module (1.8.5) software in Genome Studio (V2010.3). The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology. The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.<br>For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se. <br>
DOI: 10.17044/scilifelab.14687271
title: CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

description: <b>Welcome to the the CSAW-M dataset homepage</b><br>This page includes the files and metadata related to the <em>CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer</em>.
CSAW-M is collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image <em>density</em> as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We trained deep learning models on CSAW-M to estimate the masking level, and showed that the estimated masking is significantly more predictive of screening participants diagnosed with <em>interval</em> and <em>large invasive</em> cancers — without being explicitly trained for these tasks — than its breast density counterparts.
Please find the paper corresponding to our work here and the GitHub repo here.<b>CSAW-M Research Use License</b><b>Please read carefully all the terms and conditions of the CSAW-M Research Use License.</b><br>
<b>How to access the dataset</b>:<br>If you want to get access to the data, please use the "Request access to files" option above (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.<b>If you use this Work, please cite our paper:</b><pre><sub>@article{sorkhei2021csaw,
 title={CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer},
 author={Sorkhei, Moein and Liu, Yue and Azizpour, Hossein and Azavedo, Edward and Dembrower, Karin and Ntoula, Dimitra and Zouzos, Athanasios and Strand, Fredrik and Smith, Kevin},
 year={2021}
}</sub></pre>
DOI: 10.7483/opendata.cms.yzy3.5t0a
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.14687271.v1
title: CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

description: <b>Welcome to the the CSAW-M dataset homepage</b><br>This page includes the files and metadata related to the <em>CSAW-M, a curated dataset of mammograms with expert assessments of the masking of cancer</em>.
CSAW-M is collected from over 10,000 individuals and annotated with potential masking. In contrast to the previous approaches which measure breast image <em>density</em> as a proxy, our dataset directly provides annotations of masking potential assessments from five specialists. We trained deep learning models on CSAW-M to estimate the masking level, and showed that the estimated masking is significantly more predictive of screening participants diagnosed with <em>interval</em> and <em>large invasive</em> cancers — without being explicitly trained for these tasks — than its breast density counterparts.
Please find the paper corresponding to our work here and the GitHub repo here.<b>Terms of use:</b><i><br></i>Our dataset is licensed under CC BY-NC-ND 4.0 for <em>non-commercial</em> use only, and for research related to that of this work. Here we ask users to agree to our terms and conditions when requesting the dataset.I request access to the CSAW-M dataset for research purposes and agree to the following terms:
I will not share the download link with anybody.
I will not attempt to re-identify the subjects or use the data for any illegal purposes.
In the unlikely case of re-identification of any individuals in the dataset, I will immediately inform the authors.
Upon request from the authors at any time, I am obliged to delete any copy of the data I have.
I will require anyone in my team who uses this data, or whoever I share the data with, to comply with the terms mentioned on this page.
By filling the request form, I will allow the authors to keep the information I provide (which is used by the authors to simply keep a record of who requested the dataset). Also, I will allow the authors to use this information to contact me (e.g. in case the dataset is updated).
I understand that the terms of use mentioned on this page are subject to change. I will be informed about such changes and comply with them.
If I violate any of the terms above, I will immediately delete the data and will not retain any portion of it.
By sending a request to access the dataset, I agree to all the above-mentioned terms.
<b>How to access the dataset</b>:<br>If you want to get access to the data, please use the "Request access to files" option that will appear above soon (currently, non-Swedish researchers need to have a general figshare account to be able to to request access). We will ask you to agree to our terms of conditions and provide us with some information about what you will use the data for. We will then receive the request and process it, after which you would be able to download all the files.
DOI: 10.15468/dl.ppcq7u
title: Occurrence Download

description: A dataset containing 1960 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gymnorhamphichthys Ellis, 1912"
 ]
}

 The dataset includes 1960 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071187-241126133413365/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.j16u.9zun
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH350_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.48579/pro/ieqwvz
title: Collection of rasters and social ecological indicators for Zone Atelier Armorique

description: Open, world wide rasters (biogeoregions, land surface temperature, normalised difference vegetation index, population density and land cover) are cropped with respect to the polygon of the Zone Atelier Environmental Urban so as to compute indicators. This dataset contains the shapefile, rasters for variables BIOG (biogeoregion), LSTD (land surface temperature day), LSTN (land surface temperature night), NDVI (normalized difference vegetation index), GPW4 (population density), and LC (land cover) cropped with respect to the shapefile, and social ecological indicators for these variables computed as averages or quantiles.
DOI: 10.7483/opendata.cms.ibnh.hb6g
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.53p59c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hymenalia elongata Pic, 1925",
 "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/0071189-241126133413365/datasets/export for details.
DOI: 10.17632/n2j4yfnt3x.1
title: Adversarial Attacks for Low Light Image Enhancement

description: This dataset is an image repository that evaluates adversarial robustness by introducing 12 adversarial examples, each leveraging a known adversarial attack or noise perturbation. The repository contains a total of 4970 digital images through the application of AEs to five different image databases (i.e., MEF, LIME, LOLv1, LOLv2-R, LOLv2-S) that include pictures of landscapes, objects, or locations with low, normal, or high illumination conditions. We incorporate original images from the five databases utilized alongside the adversarial examples to facilitate easy access and distribution of this dataset.
DOI: 10.7483/opendata.cms.6zxy.u7fb
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/dvrhhs88th.1
title: The Aesthetic Effects of GLP-1 Receptor Agonists: A Review

description: Supplemental material for our study "The Aesthetic Effects of GLP-1 Receptor Agonists: A Scoping Review"
DOI: 10.17632/nmtcnccbhn.1
title: Training dataset for OM apportionment in the sediments of SLEG

description: The set contained N = 36 (18 duplicated) samples from two classes of marine and terrestrial end members (0 and 1 respectively). This dataset includes measurements of isotopic (δ13C, δ15N) and chemical composition of the sedimentary OM and homologous n-alkanes.
DOI: 10.7483/opendata.cms.tnk2.pll5
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/mwdzp4m58x.1
title: Unseen test dataset for OM apportionment in the sediments of SLEG

description: SLEG stations data, each can be fed separately to the trained regression models for prediction of terrestrial OM proportion in sediments.
DOI: 10.17632/bk2drsh6h8.1
title: R codes for Sedimentary OM analyses

description: This R script contains the full workflow and analysis used in the study titled "Integrated Machine Learning and Multivariate Analyses to Apportion the Different Organic Matter Sources to the Coastal Sediment Sink: A Case Study in Organic Geochemistry".
DOI: 10.7483/opendata.cms.krff.zql2
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/bvjwf978bm.1
title: Data under a controlled environment on pollen viability, stigma condition, and plant physiological status as affected by high temperature and drought during the anthesis stage of rice (Oryza sativa var. MR253)

description: This data is for Data In Brief journal submission. This data obtain from the research on High temperature and Drought to rice anthesis stage for MR253 variety.
DOI: 10.7483/opendata.cms.pvxb.c93z
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/ywx6xjm8nk.1
title: Tracking the Multiple Sources of Sediment within Agricultural Catchments in Northeast China

description: This dataset comprises particle size and 31 geochemical elements (Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, U, Th, Sr, Sb, Bi, V, Ca, P, La, Cr, Ba, Mg, Ti, Al, Na, K, Sc, Tl, Hg, and Ga) from source and target sediment samples within the Yingmincun catchment in the Mollisol region of Northeast China.

The source samples include 57 topsoil samples (48 from cultivated land and 9 from unsealed roads) and 41 subsoil samples (26 from ephemeral gullies and 15 from gully banks). The target sediment samples, collected during the rainy seasons from 2018 to 2020, consist of 63 suspended sediment samples and 10 deposited sediment samples.

The samples were ground and sieved through a 2 mm mesh. The particle size was determined by using a Mastersizer 2000 analyser (Malvern Instruments, Malvern, UK). The classification of grain size followed the United States Department of Agriculture (USDA) scale standard (USDA, 1951). After grinding and sieving all samples to &lt; 63 μm, a total 31 geochemical elements (Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, As, U, Th, Sr, Sb, Bi, V, Ca, P, La, Cr, Ba, Mg, Ti, Al, Na, K, Sc, Tl, Hg and Ga) were measured by using inductively coupled plasma mass spectrometry (ICP‒MS) (NexION, 300X, Perkin Elmer Inc., Waltham, MA) at the Shaanxi Provence Land Engineering Construction Group Laboratory, China.
DOI: 10.17632/tgdp9h5wbr.1
title: Predicting heart attack using time series data

description: Machine learning can be used on the data to predict heart attacks in advance.
DOI: 10.7483/opendata.cms.vjtx.ln5d
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.14743413
title: Dataset of SARS-CoV-2 wastewater data from Enköping, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Enköping, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see<br>https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br>The dataset started week 13 2021 and updates weekly.<br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/
DOI: 10.17044/scilifelab.14743413.v1
title: Dataset of SARS-CoV-2 wastewater data from Enköping, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Enköping, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see<br>https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br>The dataset started week 13 2021 and updates weekly.<br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/
DOI: 10.17044/scilifelab.14743425
title: Dataset of SARS-CoV-2 wastewater data from Östhammar, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Östhammar, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Östhammar started week 14 2021. <br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>