cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.20509362
title: Dissecting the mechanisms of environment sensitivity of smart probes for quantitative assessment of membrane properties

description:  
<strong>General Description</strong>
<br>
It contains data sets from Ragaller et al, Open Biology, 2022 (https://doi.org/10.1098/rsob.220175).<br>

<br>
It contains Generalized Polarization (GP) values, GP profiles, GP maps, raw spectral images, MD Simulation snapshots, tilt angle distributions, partial density profiles, relaxation time and stokes shift
<br>
<br>
<strong>Abstract<br>
</strong>
The plasma membrane serves as a crucial platform for a multitude of cellular processes. Its collective biophysical properties are largely determined by the structural diversity of the different lipid species it accommodates. Therefore, a detailed investigation of biophysical properties of the plasma membrane is of utmost importance for a comprehensive understanding of biological processes occurring therein. During the past two decades several environment-sensitive probes have been popular tools to investigate membrane properties. Although these probes are assumed to report on membrane order in similar ways, their individual mechanisms remain to be elucidated. In this study, using model membrane systems, we studied the probes Pro12A, NR12S and NR12A in depth and examined their sensitivity to parameters with potential biological implications, such as the degree of lipid saturation, double bond position and configuration, phospholipid headgroup and cholesterol content. Applying spectral imaging together with atomistic molecular dynamics simulations and time-dependent fluorescent shift analyses, we unravelled individual sensitivities of these probes to different biophysical properties, their distinct localizations and specific relaxation processes in membranes. Overall, Pro12A, NR12S and NR12A serve together as a toolbox with a wide range of applications allowing to select the most appropriate probe for each specific research question. 
<br>
<strong>Data usage</strong>
<br>
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>
<br>
Excel files for graphs
Microscopy Images
<br>
<strong>Software to open files</strong>
<br>
.csv - Microsoft Excel<br>
.tif, .lsm - Fiji (https://imagej.net/Fiji.html#Downloads)<br>
.pzfx - GraphPad Prism<br>
.svg - Inkscape (https://inkscape.org/)
DOI: 10.17044/scilifelab.20509362.v1
title: Dissecting the mechanisms of environment sensitivity of smart probes for quantitative assessment of membrane properties

description:  
<strong>General Description</strong>
<br>
It contains data sets from Ragaller et al, Open Biology, 2022 (https://doi.org/10.1098/rsob.220175).<br>

<br>
It contains Generalized Polarization (GP) values, GP profiles, GP maps, raw spectral images, MD Simulation snapshots, tilt angle distributions, partial density profiles, relaxation time and stokes shift
<br>
<br>
<strong>Abstract<br>
</strong>
The plasma membrane serves as a crucial platform for a multitude of cellular processes. Its collective biophysical properties are largely determined by the structural diversity of the different lipid species it accommodates. Therefore, a detailed investigation of biophysical properties of the plasma membrane is of utmost importance for a comprehensive understanding of biological processes occurring therein. During the past two decades several environment-sensitive probes have been popular tools to investigate membrane properties. Although these probes are assumed to report on membrane order in similar ways, their individual mechanisms remain to be elucidated. In this study, using model membrane systems, we studied the probes Pro12A, NR12S and NR12A in depth and examined their sensitivity to parameters with potential biological implications, such as the degree of lipid saturation, double bond position and configuration, phospholipid headgroup and cholesterol content. Applying spectral imaging together with atomistic molecular dynamics simulations and time-dependent fluorescent shift analyses, we unravelled individual sensitivities of these probes to different biophysical properties, their distinct localizations and specific relaxation processes in membranes. Overall, Pro12A, NR12S and NR12A serve together as a toolbox with a wide range of applications allowing to select the most appropriate probe for each specific research question. 
<br>
<strong>Data usage</strong>
<br>
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>
<br>
Excel files for graphs
Microscopy Images
<br>
<strong>Software to open files</strong>
<br>
.csv - Microsoft Excel<br>
.tif, .lsm - Fiji (https://imagej.net/Fiji.html#Downloads)<br>
.pzfx - GraphPad Prism<br>
.svg - Inkscape (https://inkscape.org/)
DOI: 10.7483/opendata.cms.akej.qfn8
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.wrh8uv
title: Occurrence Download

description: A dataset containing 2004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Crax alberti Fraser, 1852"
 ]
}

 The dataset includes 2004 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071198-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17044/scilifelab.20514192.v1
title: COI reference sequences from BOLD DB

description: Dataset description
This item contains COI (mitochondrial cytochrome oxidase subunit I) sequences<br>
collected from the BOLD database. The fasta file<br>
bold_clustered_cleaned.fasta.gz has record ids that can be queried in the Public<br>
Data Portal<br>
and each fasta header contains the taxonomic ranks + the BIN ID assigned to the<br>
record. The taxonomic information for each record is also given in the tab-separated<br>
file bold_info_filtered.tsv.gz.<br>
<br>
The dataset was last created on February 18, 2022.
<br>
<br>
Methods
The code used to generate this dataset consists of a snakemake workflow wrapped<br>
into a python package that can be installed with conda<br>
(`conda install -c bioconda coidb`).<br>

Firstly sequence and taxonomic information for records in the BOLD database is<br>
downloaded from the GBIF Hosted Datasets.<br>
This data is then filtered to only keep records annotated as 'COI-5P' and assigned<br>
to a BIN ID. The taxonomic information is parsed in order to assign species names<br>
and resolve higher level ranks for each BIN ID. Sequences are processed to remove<br>
gap characters and leading and trailing `N`s. After this, any sequences with<br>
remaining non-standard characters are removed.<br>
Sequences are then clustered at 100% identity using vsearch<br>
(Rognes _et al._ 2016). This clustering is done separately for sequences assigned<br>
to each BIN ID.   
<br>
For more information, see https://github.com/biodiversitydata-se/coidb
DOI: 10.7483/opendata.cms.q9el.9llr
title: /TprimeBToTH_TLep_Hbb_LH_MT2700_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20517336
title: A Multi-Parametric and High-Throughput Platform for Host-Virus Binding Screens

description: <strong>General information</strong>
<br>
This item containst data sets for Schlegel et al, Nano Letters, 2023. 
DOI: https://doi.org/10.1021/acs.nanolett.2c04884
<br>
It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files. 
<br>
<strong>Abstract</strong>
Speed is key during infectious disease outbreaks. It
is essential, for example, to identify critical host binding factors to
pathogens as fast as possible. The complexity of host plasma
membrane is often a limiting factor hindering fast and accurate
determination of host binding factors as well as high-throughput
screening for neutralizing antimicrobial drug targets. Here, we
describe a multiparametric and high-throughput platform tackling
this bottleneck and enabling fast screens for host binding factors as
well as new antiviral drug targets. The sensitivity and robustness of
our platform were validated by blocking SARS-CoV-2 particles
with nanobodies and IgGs from human serum samples.
<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.
 
<strong>Data Content</strong>
Excel files for graphs
Microscopy Images
Flow cytometry data 
<br>
<strong>Software to open files:</strong>
.csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel 
.xlsx: Microsoft Excel 
.tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads)
.pzfx: GraphPad Prism
.svg: Inkscape (https://inkscape.org/)
.fcs: FCS Express
.pdf: AdobeAcrobat or Mozilla Firefox
.ijm: Fiji (https://imagej.net/software/fiji/downloads)
DOI: 10.7483/opendata.cms.qrdf.adx0
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20517336.v1
title: A Multi-Parametric and High-Throughput Platform for Host-Virus Binding Screens

description: <strong>General information</strong>
<br>
This item containst data sets for Schlegel et al, Nano Letters, 2023. 
DOI: https://doi.org/10.1021/acs.nanolett.2c04884
<br>
It contains confocal images, lattice light sheet images, flow cytometry data, compiled data as excle sheet and raw figure files. 
<br>
<strong>Abstract</strong>
Speed is key during infectious disease outbreaks. It
is essential, for example, to identify critical host binding factors to
pathogens as fast as possible. The complexity of host plasma
membrane is often a limiting factor hindering fast and accurate
determination of host binding factors as well as high-throughput
screening for neutralizing antimicrobial drug targets. Here, we
describe a multiparametric and high-throughput platform tackling
this bottleneck and enabling fast screens for host binding factors as
well as new antiviral drug targets. The sensitivity and robustness of
our platform were validated by blocking SARS-CoV-2 particles
with nanobodies and IgGs from human serum samples.
<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.
 
<strong>Data Content</strong>
Excel files for graphs
Microscopy Images
Flow cytometry data 
<br>
<strong>Software to open files:</strong>
.csv: Fiji (https://imagej.net/software/fiji/downloads) or Microsoft Excel 
.xlsx: Microsoft Excel 
.tif, .lsm: Fiji (https://imagej.net/software/fiji/downloads)
.pzfx: GraphPad Prism
.svg: Inkscape (https://inkscape.org/)
.fcs: FCS Express
.pdf: AdobeAcrobat or Mozilla Firefox
.ijm: Fiji (https://imagej.net/software/fiji/downloads)
DOI: 10.17044/scilifelab.20514192.v2
title: COI reference sequences from BOLD DB

description: Dataset description
This item contains COI (mitochondrial cytochrome oxidase subunit I) sequences collected from the BOLD database. The fasta file bold_clustered_cleaned.fasta.gz has record ids that can be queried in the Public Data Portal and each fasta header contains the taxonomic ranks + the BIN ID assigned to the record. The taxonomic information for each record is also given in the tab-separated file bold_info_filtered.tsv.gz.<br>
The file bold_clustered.sintax.fasta.gz is directly compatible with the SINTAX algorithm in vsearch while files bold_clustered.assignTaxonomy.fasta.gz and bold_clustered.addSpecies.fasta.gz are directly compatible with the assignTaxonomy and addSpecies functions from DADA2, respectively.  The dataset was last created on December 16, 2022<br>

<strong>Methods</strong>
The code used to generate this dataset consists of a snakemake workflow wrapped into a python package that can be installed with conda (`conda install -c bioconda coidb`).  Firstly sequence and taxonomic information for records in the BOLD database is downloaded from the GBIF Hosted Datasets. This data is then filtered to only keep records annotated as 'COI-5P' and assigned to a BIN ID. The taxonomic information is parsed in order to assign species names and resolve higher level ranks for each BIN ID. Sequences are processed to remove gap characters and leading and trailing `N`s. After this, any sequences with remaining non-standard characters are removed. Sequences are then clustered at 100% identity using vsearch (Rognes _et al._ 2016). This clustering is done separately for sequences assigned to each BIN ID.   <br>

For more information, see https://github.com/biodiversitydata-se/coidb
DOI: 10.17632/9fpyr2cdtv.1
title: Benchmark dataset and results for "Real-World Drone Routing Problems with Quantum Computing" experimentation

description: This dataset contains all the instances employed in the experimentation described in the paper "Solving Drone Routing Problems with Quantum Computing: A hybrid approach combining quantum annealing and gate-based paradigms". 

Results discussed in the paper are also included in this dataset.
DOI: 10.7483/opendata.cms.uscx.9d58
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tiqx.cxol
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.4fcc.1gjt
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20746576
title: Growth analysis of Apilactobacillus kunkeei strains

description: Growth analysis of <em>Apilactobacillus kunkeei</em> strains used for manuscript "Dyrhage et al. Genome Evolution of a Symbiont Population for Pathogen Defence in Honeybees, Genome Biology and Evolution, 2022."
<br>
The dataset contains the raw data from the Bioscreen C instrument, the column names and the RMarkdown scripts for analysis of the individual growth experiments. A summary file (EXP-20-BQ4058_growthanalysis.summary.Rmd) merges the results from the individual experiments to produce the final summary of growth kinetics for all the tested strains.
<br>
*<em>growthanalysis.csv</em>: raw growthdata from Bioscreen C
*<em>growthanalysis.colnames.csv</em>: sample IDs used in the corresponding *<em>Akunkeei.Rmd </em>scripts to associate plate position with the sample.
*<em>Akunkeei.Rmd</em>:<em> </em>RMarkdown script to analyze the growthdata using the growthcurver package
<em>EXP-20-BQ4058_growthanalysis.summary.Rmd</em>: RMarkdown script to combine the collected growthdata and to generate the results that are part of "Dyrhage et al. Genome Evolution of a Symbiont Population for Pathogen Defence in Honeybees, Genome Biology and Evolution, 2022."
DOI: 10.17044/scilifelab.20746576.v1
title: Growth analysis of Apilactobacillus kunkeei strains

description: Growth analysis of <em>Apilactobacillus kunkeei</em> strains used for manuscript "Dyrhage et al. Genome Evolution of a Symbiont Population for Pathogen Defence in Honeybees, Genome Biology and Evolution, 2022."
<br>
The dataset contains the raw data from the Bioscreen C instrument, the column names and the RMarkdown scripts for analysis of the individual growth experiments. A summary file (EXP-20-BQ4058_growthanalysis.summary.Rmd) merges the results from the individual experiments to produce the final summary of growth kinetics for all the tested strains.
<br>
*<em>growthanalysis.csv</em>: raw growthdata from Bioscreen C
*<em>growthanalysis.colnames.csv</em>: sample IDs used in the corresponding *<em>Akunkeei.Rmd </em>scripts to associate plate position with the sample.
*<em>Akunkeei.Rmd</em>:<em> </em>RMarkdown script to analyze the growthdata using the growthcurver package
<em>EXP-20-BQ4058_growthanalysis.summary.Rmd</em>: RMarkdown script to combine the collected growthdata and to generate the results that are part of "Dyrhage et al. Genome Evolution of a Symbiont Population for Pathogen Defence in Honeybees, Genome Biology and Evolution, 2022."
DOI: 10.7483/opendata.cms.45wt.2xlf
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.20749174
title: Supplementary information for manuscript "<i>Apilactobacillus kunkeei</i><i> </i><i>releases RNA-associated membrane vesicles and proteinaceous nanoparticles</i>"

description: Supplementary information for manuscript "<i>Apilactobacillus kunkeei</i><i> </i><i>releases RNA-associated membrane vesicles and proteinaceous nanoparticles</i>".The supplementary information comprises imaging data obtained from Transmission (TEM) and Scanning Electron Microscopy (SEM) as well as negative stain TEM (nsTEM). Electron micrographs have been obtained from whole cells of two <i>Apilactobacillus kunkeei </i>strains, A0901 and A1401, by TEM and SEM, as well as from isolated secreted nanoparticles (nSTEM). Those particles have been described as membranous RNA-associated membrane vesicles (MVs) and proteinaceous extracellular particles (ECPs).The compressed folder contains the imaging files. Files and subdirectories are described in manifest.txt. The <i>imaging</i> folder contains a file, file.description.ecp.imaging.xlsx, with metainformation on the collected electron microscopy images.
DOI: 10.17044/scilifelab.20749174.v1
title: Supplementary information for manuscript "<i>Apilactobacillus kunkeei</i><i> </i><i>releases RNA-associated membrane vesicles and proteinaceous nanoparticles</i>"

description: Supplementary information for manuscript "<i>Apilactobacillus kunkeei</i><i> </i><i>releases RNA-associated membrane vesicles and proteinaceous nanoparticles</i>".The supplementary information comprises imaging data obtained from Transmission (TEM) and Scanning Electron Microscopy (SEM) as well as negative stain TEM (nsTEM). Electron micrographs have been obtained from whole cells of two <i>Apilactobacillus kunkeei </i>strains, A0901 and A1401, by TEM and SEM, as well as from isolated secreted nanoparticles (nSTEM). Those particles have been described as membranous RNA-associated membrane vesicles (MVs) and proteinaceous extracellular particles (ECPs).The compressed folder contains the imaging files. Files and subdirectories are described in manifest.txt. The <i>imaging</i> folder contains a file, file.description.ecp.imaging.xlsx, with metainformation on the collected electron microscopy images.
DOI: 10.7483/opendata.cms.a53u.5f59
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.20751373
title: Additional data to: A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea

description: Additional data to the publication 'A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea'. 
Short description of the publication: A dataset covering spatiotemporal variation in prokaryotic and eukaryotic microbial communities and physicochemical parameters of the Baltic Sea. Between January 2019 and February 2020, 281 transect-time course samples and 65 samples for protocol testing were collected from 19 stations in the Baltic Sea, Kattegat and Skagerrak. We analysed the samples with 18S ribosomal RNA (rRNA) gene and 16S rRNA gene metabarcoding, to capture the eukaryotic and prokaryotic diversity, respectively. Complementary analyses were conducted on the same samples by the Swedish National Marine Monitoring Program: Microscopy analyses were performed on samples preserved in Lugol’s solution for phytoplankton identification, estimation of cell volumes and cell counts. Samples from nine stations were quantified for phototrophic picoplankton using flow cytometry and fluorescence microscopy. Chlorophyll-<em>a</em> content was assessed in the water as a proxy for total phytoplankton biomass and a range of physicochemical parameters were captured.
Additional data include: 
-metadata file on metabarcoding samples with physicochemical parameters,
-data tables with: additional physicochemical data, data on phytoplankton abundance counts, biovolume and carbon biomass (from light microscopy), abundance of picoplankton (epifluorescence microscopy and flow cytometry), chlorophyll a content (these data can also be accessed through SHARKweb)
-R scripts and files associated with the 16S & 18S metabarcoding data (ASV, sequence & taxonomy table)
-table with file names of sequencing data (submitted to ENA)
-spike-in DNA sequences, added before DNA extraction for normalisation of the samples sequenced with metabarcoding
-instructions on how to access metadata through SHARKweb
DOI: 10.17044/scilifelab.20751373.v1
title: Additional data to: A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea

description: Additional data to the publication 'A comprehensive dataset on spatiotemporal variation of microbial plankton communities in the Baltic Sea'. 
Short description of the publication: A dataset covering spatiotemporal variation in prokaryotic and eukaryotic microbial communities and physicochemical parameters of the Baltic Sea. Between January 2019 and February 2020, 281 transect-time course samples and 65 samples for protocol testing were collected from 19 stations in the Baltic Sea, Kattegat and Skagerrak. We analysed the samples with 18S ribosomal RNA (rRNA) gene and 16S rRNA gene metabarcoding, to capture the eukaryotic and prokaryotic diversity, respectively. Complementary analyses were conducted on the same samples by the Swedish National Marine Monitoring Program: Microscopy analyses were performed on samples preserved in Lugol’s solution for phytoplankton identification, estimation of cell volumes and cell counts. Samples from nine stations were quantified for phototrophic picoplankton using flow cytometry and fluorescence microscopy. Chlorophyll-<em>a</em> content was assessed in the water as a proxy for total phytoplankton biomass and a range of physicochemical parameters were captured.
Additional data include: 
-metadata file on metabarcoding samples with physicochemical parameters,
-data tables with: additional physicochemical data, data on phytoplankton abundance counts, biovolume and carbon biomass (from light microscopy), abundance of picoplankton (epifluorescence microscopy and flow cytometry), chlorophyll a content (these data can also be accessed through SHARKweb)
-R scripts and files associated with the 16S & 18S metabarcoding data (ASV, sequence & taxonomy table)
-table with file names of sequencing data (submitted to ENA)
-spike-in DNA sequences, added before DNA extraction for normalisation of the samples sequenced with metabarcoding
-instructions on how to access metadata through SHARKweb
DOI: 10.7483/opendata.cms.atzq.n191
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH175_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.5281/zenodo.13364438
title: MASCOT data file
DOI: 10.5281/zenodo.13364439
title: MASCOT data file
DOI: 10.7483/opendata.cms.2kvr.tzsp
title: /TprimeBToTH_TLep_Hbb_LH_MT2800_MH175_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM