cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.20171347.v3
title: References and test datasets for the Cactus pipeline

description: Overview
This item contains references and test datasets for the Cactus pipeline.
Cactus (Chromatin ACcessibility and Transcriptomics  Unification Software) is an mRNA-Seq and ATAC-Seq analysis pipeline that aims to provide advanced molecular insights on the conditions under study. 
<br>
Test datasets
The test datasets contain all data needed to run Cactus in each of the 4 supported organisms. This include ATAC-Seq and mRNA-Seq data (*.fastq.gz), parameter files (*.yml) and design files (*.tsv). They were were created for each species by downloading publicly available datasets with fetchngs (Ewels et al., 2020) and subsampling reads to the minimum required to have enough DAS (Differential Analysis Subsets) for enrichment analysis.
Datasets downloaded:
- Worm and Humans: GSE98758
- Fly: GSE149339
- Mouse: GSE193393
<br>
References
One of the goals of Cactus is to make the analysis as simple and fast as possible for the user while providing detailed insights on molecular mechanisms. This is achieved by parsing all needed references for the 4 ENCODE (Dunham et al., 2012; Stamatoyannopoulos et al., 2012; Luo et al., 2020) and modENCODE (THE MODENCODE CONSORTIUM et al., 2010; Gerstein et al., 2010) organisms (human, M. musculus, D. melanogaster and C. elegans). This parsing step was done with a Nextflow pipeline with most tools encapsulated within containers for improved efficiency and reproducibility and to allow the creation of customized references.
Genomic sequences and annotations were downloaded from Ensembl (Cunningham et al., 2022). The ENCODE API (Luo et al., 2020) was used to download the CHIP-Seq profiles of 2,714 Transcription Factors (TFs) (Landt et al., 2012; Boyle et al., 2014) and chromatin states in the form of 899 ChromHMM profiles (Boix et al., 2021; van der Velde et al., 2021) and 6 HiHMM profiles (Ho et al., 2014). Slim annotations (cell, organ, development, and system) were parsed and used to create groups of CHIP-Seq profiles that share the same annotations, allowing users to analyze only CHIP-Seq profiles relevant to their study. 2,779 TF motifs were obtained from the Cis-BP database (Lambert et al., 2019). GO terms and KEGG pathways were obtained via the R packages AnnotationHub (Morgan and Shepherd, 2021) and clusterProfiler (Yu et al., 2012; Wu et al., 2021), respectively.
<br>
Documentation
More information on how to use Cactus and how references and test datasets were created is available on the documentation website: https://github.com/jsalignon/cactus.
DOI: 10.7483/opendata.cms.tl21.w0b6
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kfge.qfmv
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20208764
title: Targeted scRNA-seq and AbSeq of human CAR-T cell infusion product from 24 cancer patients

description: <b>Backgroud information</b>The dataset contains single cell targeted RNA sequencing (RNAseq) and targeted antibody-oligonucleotide conjugates sequencing (Abseq) data from chimeric antigen receptor (CAR)-engineered T cells used to treat each individual cancer patients in a clinical study. The starting material was in all cases autologous T cells harvested from peripheral blood of patients. The data is collected from 24 participants of which 23 were adult patients with relapsed or refractory B cell lymphoma and one was a pediatric patient with relapsed B cell acute lymphoblastic leukemia. The data were generated as part of a study by Sarén et. al,<i> </i><i>Clinical Cancer Research</i> (2023).Targeted RNA and protein single-cell libraries were generated using the BD Rhapsody™ platform (BD Biosciences). Cells were labeled with sample tags from the BD Human Immune Single-Cell Multiplexing Kit and BD Ab-seq Ab-Oligos and live cells were collected by flow cytometry. CAR-T cells were loaded on BD Rhapsody cartridge and mRNA captured with cell capture beads and used as template for cDNA synthesis. Four separate targeted libraries were produced and pooled for paired-end sequencing on NovaSeq 6000 S1 sequencer (Illumina) at the SNP&SEQ Technology Platform (Uppsala, Sweden).<b>Terms of access</b>Sequencing data generated during the current study are not publicly available due to the European General Data Protection Regulation (GDPR) to protect patients’ privacy but are available from the corresponding author on reasonable request (see contact info). The dataset is only to be used for research that is seeking to advance the understanding of CAR-T cell treatment of cancer.<b>Ancillary datasets and code</b>Processed RNAseq and AbSeq data, in the form of raw and normalized count matrices, are available on BioStudies (Accession: E-MTAB-12407).R code used to process the data is available on the study GitHub repository:https://github.com/magnessa/EudraCT_2016-004043-36<br><br>
DOI: 10.7483/opendata.cms.bzrl.p4pt
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20208764.v1
title: Targeted scRNA-seq and AbSeq of human CAR-T cell infusion product from 24 cancer patients

description: <b>Backgroud information</b>The dataset contains single cell targeted RNA sequencing (RNAseq) and targeted antibody-oligonucleotide conjugates sequencing (Abseq) data from chimeric antigen receptor (CAR)-engineered T cells used to treat each individual cancer patients in a clinical study. The starting material was in all cases autologous T cells harvested from peripheral blood of patients. The data is collected from 24 participants of which 23 were adult patients with relapsed or refractory B cell lymphoma and one was a pediatric patient with relapsed B cell acute lymphoblastic leukemia. The data were generated as part of a study by Sarén et. al,<i> </i><i>Clinical Cancer Research</i> (2023).Targeted RNA and protein single-cell libraries were generated using the BD Rhapsody™ platform (BD Biosciences). Cells were labeled with sample tags from the BD Human Immune Single-Cell Multiplexing Kit and BD Ab-seq Ab-Oligos and live cells were collected by flow cytometry. CAR-T cells were loaded on BD Rhapsody cartridge and mRNA captured with cell capture beads and used as template for cDNA synthesis. Four separate targeted libraries were produced and pooled for paired-end sequencing on NovaSeq 6000 S1 sequencer (Illumina) at the SNP&SEQ Technology Platform (Uppsala, Sweden).<b>Terms of access</b>Sequencing data generated during the current study are not publicly available due to the European General Data Protection Regulation (GDPR) to protect patients’ privacy but are available from the corresponding author on reasonable request (see contact info). The dataset is only to be used for research that is seeking to advance the understanding of CAR-T cell treatment of cancer.<b>Ancillary datasets and code</b>Processed RNAseq and AbSeq data, in the form of raw and normalized count matrices, are available on BioStudies (Accession: E-MTAB-12407).R code used to process the data is available on the study GitHub repository:https://github.com/magnessa/EudraCT_2016-004043-36<br><br>
DOI: 10.17044/scilifelab.20310249
title: Intrinsic lipolysis rate for systematic design of lipid-based formulations

description: Lipid-based formulations (LBFs) are used by the pharmaceutical industry in oral delivery systems for both poorly water-soluble drugs and biologics. Digestibility is key for the performance of LBFs and in vitro lipolysis is commonly used to compare the digestibility of LBFs. Results from in vitro lipolysis experiments depend highly on the experimental conditions and formulation characteristics, such as droplet size (which defines the surface area available for digestion) and interfacial structure. This study introduced the intrinsic lipolysis rate (ILR) as a surface area-independent approach to compare lipid digestibility.
<br>
The data contained in this dataset has been collected in the period from July 2021 until March 2022 at the Biomedical Center (BMC), Uppsala University.
The dataset contains two types of data: 1) dynamic light scattering (DLS) measurements of acylglycerol nanoemulsion droplets and 2) in vitro pH stat lipolysis data obtained from the digestion of acylglycerol nanoemulsions. 
<br>
Nineteen acylglycerol nanoemulsions were prepared with polysorbate 80 as stabilizer and digested in vitro. The experiments were conducted in triplicate. The data contained in this dataset is arranged in folders representing the nineteen acylglycerol nanoemulsions. The folder title gives information about the acylglycerol in the nanoemulsion and the concentration of polysorbate 80 used to stabilize the nanoemulsion. 
<br>
The hydrodynamic diameter of the nanoemulsion droplets was measured using a Litesizer 500 (Anton Paar, Graz, Austria). DLS measurements were conducted at 37 °C using the instrument’s automatic setting for adjustment of the focus and measurement angle. 
The in vitro pH stat lipolysis was conducted using a titrator (907 Titrando, Metrohm, Herisau, Switzerland) connected to a 10 ml burette, a pH electrode (iUnitrode with Pt 1000, Metrohm, Herisau, Switzerland) and a propeller stirrer. Lipolysis was conducted for 90 min using 0.6 M NaOH as the titrant when digesting nanoemulsions and 0.2 M NaOH when digesting the lipolysis medium (i.e., fasted state simulated intestinal fluid) without nanoemulsion (‘blank’). After lipolysis, the pH was rapidly raised to 9 to estimate the amount of unionized fatty acids (i.e., termed ‘back titration’; BT).  
<br>
DOI: 10.17044/scilifelab.20289000
title: Forest Fire Influence on Tomicus piniperda-Associated Fungal Communities and Phloem Nutrient Availability of Colonized Pinus sylvestris

description: The study presents results of fungal metabarcoding community data from bark beetles and phloeme collected in burned and unburned forest sites in central Sweden and phloem chemistry data.
<br>
The folder "tomicus_2021" contains data and scripts used to perform statistical analysis and generate figures presented in the paper "Fire Influence on Tomicus piniperda-Associated Fungal Communities and Phloem Nutrient Availability of Colonized Pinus sylvestris" and its supplementary material. 
<br>
The folder "Corrected Tables" contains excel files with all the final tables presetned in the paper and the supplementary materail.
<br>
The files pb_466_001 and 002 contain FASTQ files from the pacbio run. These can be demultiplexed with the two primer files (Primer-ITS4_Tag-setup.txt and Primer_fITS9_Tag-setup.txt) that contain info on the tag sequence for each tag used.
<br>
<br>
DOI: 10.17044/scilifelab.20310249.v1
title: Intrinsic lipolysis rate for systematic design of lipid-based formulations

description: Lipid-based formulations (LBFs) are used by the pharmaceutical industry in oral delivery systems for both poorly water-soluble drugs and biologics. Digestibility is key for the performance of LBFs and in vitro lipolysis is commonly used to compare the digestibility of LBFs. Results from in vitro lipolysis experiments depend highly on the experimental conditions and formulation characteristics, such as droplet size (which defines the surface area available for digestion) and interfacial structure. This study introduced the intrinsic lipolysis rate (ILR) as a surface area-independent approach to compare lipid digestibility.
<br>
The data contained in this dataset has been collected in the period from July 2021 until March 2022 at the Biomedical Center (BMC), Uppsala University.
The dataset contains two types of data: 1) dynamic light scattering (DLS) measurements of acylglycerol nanoemulsion droplets and 2) in vitro pH stat lipolysis data obtained from the digestion of acylglycerol nanoemulsions. 
<br>
Nineteen acylglycerol nanoemulsions were prepared with polysorbate 80 as stabilizer and digested in vitro. The experiments were conducted in triplicate. The data contained in this dataset is arranged in folders representing the nineteen acylglycerol nanoemulsions. The folder title gives information about the acylglycerol in the nanoemulsion and the concentration of polysorbate 80 used to stabilize the nanoemulsion. 
<br>
The hydrodynamic diameter of the nanoemulsion droplets was measured using a Litesizer 500 (Anton Paar, Graz, Austria). DLS measurements were conducted at 37 °C using the instrument’s automatic setting for adjustment of the focus and measurement angle. 
The in vitro pH stat lipolysis was conducted using a titrator (907 Titrando, Metrohm, Herisau, Switzerland) connected to a 10 ml burette, a pH electrode (iUnitrode with Pt 1000, Metrohm, Herisau, Switzerland) and a propeller stirrer. Lipolysis was conducted for 90 min using 0.6 M NaOH as the titrant when digesting nanoemulsions and 0.2 M NaOH when digesting the lipolysis medium (i.e., fasted state simulated intestinal fluid) without nanoemulsion (‘blank’). After lipolysis, the pH was rapidly raised to 9 to estimate the amount of unionized fatty acids (i.e., termed ‘back titration’; BT).  
<br>
DOI: 10.15468/dl.hbsb88
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhododendron aureum Franch."
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071197-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17044/scilifelab.20289000.v1
title: Forest Fire Influence on Tomicus piniperda-Associated Fungal Communities and Phloem Nutrient Availability of Colonized Pinus sylvestris

description: The study presents results of fungal metabarcoding community data from bark beetles and phloeme collected in burned and unburned forest sites in central Sweden and phloem chemistry data.
<br>
The folder "tomicus_2021" contains data and scripts used to perform statistical analysis and generate figures presented in the paper "Fire Influence on Tomicus piniperda-Associated Fungal Communities and Phloem Nutrient Availability of Colonized Pinus sylvestris" and its supplementary material. 
<br>
The folder "Corrected Tables" contains excel files with all the final tables presetned in the paper and the supplementary materail.
<br>
The files pb_466_001 and 002 contain FASTQ files from the pacbio run. These can be demultiplexed with the two primer files (Primer-ITS4_Tag-setup.txt and Primer_fITS9_Tag-setup.txt) that contain info on the tag sequence for each tag used.
<br>
<br>
DOI: 10.17044/scilifelab.20338869
title: High-throughput measurement of the content and properties of nano-sized bioparticles with single-particle profiler

description: This item containst data sets for Sych et al, Nature Biotechology, 2023. 
It contains raw fluroescence fluctuation data as excle sheet and raw figure files. 
<br>
Abstract:
We introduce a method, single-particle profiler (SPP), that provides single-particle information on the content and biophysical properties of thousands of particles in the size range 5-150 nm. We apply SPP to measure the mRNA encapsulation efficiency of lipid nanoparticles, viral binding efficiency of different nanobodies, and biophysical heterogeneity of liposomes, lipoproteins, exosomes and viruses.  
<br>
 
<strong>Data usage</strong>
Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License.
<br>
<strong>Data Content</strong>
FCS files as raw data (.fcs)
Excel and Prism files for graphs
<br>
# Software to open files:
.xlsx: Microsoft Excel 
.pzfx: GraphPad Prism
.svg: Inkscape (https://inkscape.org/)
.fcs: Single Particle Profiler (https://github.com/taras-sych/Single-particle-profiler)
.ipynb: Jupyter Notebook, installed as part of anaconda platform, python 3.8.8 (https://www.anaconda.com/)
.py: executed via anaconda platform, python 3.8.8 (https://www.anaconda.com/)
<br>
DOI: 10.7483/opendata.cms.jhqe.rojz
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20338869.v1
title: High-throughput measurement of the content and properties of nano-sized bioparticles with single-particle profiler

description: This item containst data sets for Sych et al, Nature Biotechology, 2023. 
It contains raw fluroescence fluctuation data as excle sheet and raw figure files. 
<br>
Abstract:
We introduce a method, single-particle profiler (SPP), that provides single-particle information on the content and biophysical properties of thousands of particles in the size range 5-150 nm. We apply SPP to measure the mRNA encapsulation efficiency of lipid nanoparticles, viral binding efficiency of different nanobodies, and biophysical heterogeneity of liposomes, lipoproteins, exosomes and viruses.  
<br>
 
<strong>Data usage</strong>
Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License.
<br>
<strong>Data Content</strong>
FCS files as raw data (.fcs)
Excel and Prism files for graphs
<br>
# Software to open files:
.xlsx: Microsoft Excel 
.pzfx: GraphPad Prism
.svg: Inkscape (https://inkscape.org/)
.fcs: Single Particle Profiler (https://github.com/taras-sych/Single-particle-profiler)
.ipynb: Jupyter Notebook, installed as part of anaconda platform, python 3.8.8 (https://www.anaconda.com/)
.py: executed via anaconda platform, python 3.8.8 (https://www.anaconda.com/)
<br>
DOI: 10.7483/opendata.cms.y3y0.x09v
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15472/serr9k
title: Caracterización de fauna y flora asociada al plan de conservación del puma (Puma concolor) en los departamentos de Arauca, Vichada y Casanare

description: Para la construcción del plan de conservación y evaluación de alternativas para asegurar la conservación del Puma en la jurisdicción de Corporinoquía, es necesario entender cómo las diversas realidades que existen en el territorio afectan a la especie y su distribución. Para esto no solo debe entenderse la presencia y distribución del Puma, sino también la presencia de las especies que interactúan con él (presas, competidores) y el estado de los ecosistemas en los que habita.
description: P. concolor utiliza una amplia diversidad de ambientes que incluyen ambientes desérticos, semi-áridos, zonas montañosas, estepa y bosques abiertos (Redford & Einsenberg 1992). La habilidad del puma para habitar en una gran diversidad de hábitats lo convierte en uno de los mamíferos carnívoros más adaptable y generalistas (Iriarte et al. 1990). De tal manera, el trabajo desarrollado se enfoca en la descripción de la flora vascular asociada al hábitat de distribución de P. concolor tomando en cuenta su composición, diversidad y estructura.
description: Se realizó una caracterización de la especie en ventanas de muestreo. Para esto se verificó en campo la información que previamente se había obtenido de fuentes secundarias, y se recolectó información primaria por medio de diversas metodologías de muestreo: cámaras trampa, transectos, la caracterización vegetal y la caracterización de la avifauna. El presente conjunto de datos consta de 1.825 registros biológicos pertenecientes a los reinos Plantae (272) y Animalia (1.553), que fueron tomados entre abril y junio del 2019 en los departamentos de Arauca, Casanare y Vichada.
DOI: 10.17044/scilifelab.20502126
title: Data and code related to "Spatiotemporal kinetics of the SRP pathway in live E. coli cells", by Volkov et al.

description:  
This item contains fluorescence microscopy data, simulated data, DNA sequence data, and analysis code related to the publication: Volkov et al., "Spatiotemporal kinetics of the SRP pathway in live E. coli cells".
The Signal recognition Particle (SRP) pathway is a universal mechanism for co-translational targeting of nascent polypeptides to the membrane bound translocation complexes, the translocons. By performing single-molecule tracking of dye-labelled SRP in living E. coli cells, we have been able to directly observe and measure the kinetics of this process. We further validated and refined our resulting reaction-diffusion model through simulated microscopy.
All raw microscopy data, simulated data, and processed data are included in the item, along with analysis code and code for figure generation.
Data has been acquired at the Dept. of Cell and Molecular Biology, Biomedical Center (BMC), Uppsala University. For technical details about data acquisition, please see the related publication Volkov et al. "Spatiotemporal kinetics of the SRP pathway in live E. coli cells".
A detailed list of files and file organization can be found in the readme text file.
DOI: 10.17044/scilifelab.20502126.v1
title: Data and code related to "Spatiotemporal kinetics of the SRP pathway in live E. coli cells", by Volkov et al.

description:  
This item contains fluorescence microscopy data, simulated data, DNA sequence data, and analysis code related to the publication: Volkov et al., "Spatiotemporal kinetics of the SRP pathway in live E. coli cells".
The Signal recognition Particle (SRP) pathway is a universal mechanism for co-translational targeting of nascent polypeptides to the membrane bound translocation complexes, the translocons. By performing single-molecule tracking of dye-labelled SRP in living E. coli cells, we have been able to directly observe and measure the kinetics of this process. We further validated and refined our resulting reaction-diffusion model through simulated microscopy.
All raw microscopy data, simulated data, and processed data are included in the item, along with analysis code and code for figure generation.
Data has been acquired at the Dept. of Cell and Molecular Biology, Biomedical Center (BMC), Uppsala University. For technical details about data acquisition, please see the related publication Volkov et al. "Spatiotemporal kinetics of the SRP pathway in live E. coli cells".
A detailed list of files and file organization can be found in the readme text file.
DOI: 10.7483/opendata.cms.vkbw.pv2m
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM