cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.25907971
title: The Escherichia coli chromosome moves to the replisome

description: In <i>Escherichia coli</i>, it is debated whether the two replisomes move independently along the two chromosome arms during replication or if they remain spatially confined. Here, we use high-throughput fluorescence microscopy to simultaneously determine the location and short-time-scale (1 s) movement of the replisome and a chromosomal locus in the same cell throughout its cell cycle. The assay is performed for several loci. We find that (<i>i</i>) the two replisomes are confined to a region of ~250 nm and ~120 nm along the cell’s long and short axis, respectively, (ii) the chromosomal loci move to and through this region sequentially based on their distance from the origin of replication, and (<i>iii</i>) when a locus is being replicated, its short time-scale movement slows down. This behavior is the same at different growth rates. In conclusion, our data supports a model with DNA moving towards spatially confined replisomes at replication.This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing.Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables.For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius.Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt.
DOI: 10.17044/scilifelab.25907971.v1
title: The Escherichia coli chromosome moves to the replisome

description: In <i>Escherichia coli</i>, it is debated whether the two replisomes move independently along the two chromosome arms during replication or if they remain spatially confined. Here, we use high-throughput fluorescence microscopy to simultaneously determine the location and short-time-scale (1 s) movement of the replisome and a chromosomal locus in the same cell throughout its cell cycle. The assay is performed for several loci. We find that (<i>i</i>) the two replisomes are confined to a region of ~250 nm and ~120 nm along the cell’s long and short axis, respectively, (ii) the chromosomal loci move to and through this region sequentially based on their distance from the origin of replication, and (<i>iii</i>) when a locus is being replicated, its short time-scale movement slows down. This behavior is the same at different growth rates. In conclusion, our data supports a model with DNA moving towards spatially confined replisomes at replication.This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing.Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables.For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius.Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt.
DOI: 10.7483/opendata.cms.z696.xmvl
title: /TprimeTprime_M-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.5r8g.0n25
title: /TprimeTprime_M-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.kicb.qwfa
title: /TprimeTprime_M-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.25966867
title: Data and code related to "A microfluidic platform for in situ studies of bacteria electroporation", by Volkov et al.

description: This item contains microscopy data, analysis code and data for figures related to the publication: Volkov et al., "A microfluidic platform for <i>in situ </i>studies of bacteria electroporation".The study presents a microfluidic platform capable of single-cell electroporation with <i>in situ </i>microscopy and demonstrates delivery of DNA into bacteria.Data has been acquired at the Dept. Materials Science and Engineering and Dept. of Cell and Molecular Biology, Uppsala University.A detailed list of files and file organization can be found in the readme text file.
DOI: 10.17044/scilifelab.25966867.v1
title: Data and code related to "A microfluidic platform for in situ studies of bacteria electroporation", by Volkov et al.

description: This item contains microscopy data, analysis code and data for figures related to the publication: Volkov et al., "A microfluidic platform for <i>in situ </i>studies of bacteria electroporation".The study presents a microfluidic platform capable of single-cell electroporation with <i>in situ </i>microscopy and demonstrates delivery of DNA into bacteria.Data has been acquired at the Dept. Materials Science and Engineering and Dept. of Cell and Molecular Biology, Uppsala University.A detailed list of files and file organization can be found in the readme text file.
DOI: 10.17044/scilifelab.25998934
title: Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle

description: The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live <i>E. coli</i> cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale.This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing.Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables.For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius.Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt.
DOI: 10.7483/opendata.cms.ltxh.h1uq
title: /TprimeTprime_M-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.25998934.v1
title: Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle

description: The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live <i>E. coli</i> cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale.This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing.Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables.For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius.Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt.
DOI: 10.7483/opendata.cms.49py.8jdq
title: /TprimeTprime_M-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/pdcp9mjy3z.1
title: Comprehensive Deepfake Detection Dataset: Real and Synthetic Frames from Roop and Akool AI Technologies

description: This dataset is a cutting-edge resource for deepfake detection, containing 110,694 frames extracted from 480 videos. It features two primary categories: deepfake and real frames. The deepfake frames (106,948) were generated from 450 videos using advanced AI tools such as Roop Faceswapper and Akool AI, while the real frames (3,746) were derived from 30 authentic videos. The dataset is meticulously curated to ensure diversity, balance, and high-quality representation, making it an invaluable resource for training and evaluating deepfake detection models.

The dataset collection process was conducted with ethical approval from Daffodil International University, ensuring adherence to ethical standards for data collection and research in deepfake detection systems.

Key Features:
Total Videos: 480 (450 deepfake, 30 real).
Frame Distribution: 106,948 deepfake frames and 3,746 real frames.
Deepfake Generation Tools: Leveraged state-of-the-art technologies like Roop Face-Swapper and Akool AI for synthetic video creation.
Demographic Diversity: Includes frames from 15 males and 15 females, ensuring varied representation across facial features, lighting conditions, and environments.
Balanced Dataset: Carefully curated for fair model training and evaluation.
Applications: Ideal for developing scalable, real-time detection systems in cybersecurity, digital forensics, and media integrity verification.

This dataset offers an unparalleled opportunity to explore and enhance deepfake detection models, addressing the challenges posed by synthetic media with a diverse and high-quality benchmark.
DOI: 10.17044/scilifelab.26064754
title: The genomic landscape of relapsed infant and childhood KMT2A-rearranged acuteleukemia

description: This dataset includes whole genome and whole exome sequencing of diagnostic, germline, and relapse samples of 21 infants and 12 children with relapsed ALL (n=21) or AML (n=12), and targeted deep sequencing of 258 samples taken during treatment for 30 infants or children with ALL or AML. All patients had an underlying rearrangement of the <i>KMT2A</i> geneWhole genome and exome sequencing was performed using Illumina TruSeq Nano protocol. Targeted sequencing libraries was prepared using Nextera XT DNA Sample Preparation Kit (Illumina) and sequenced on the Illumina MiSeq.
DOI: 10.17044/scilifelab.26064754.v1
title: The genomic landscape of relapsed infant and childhood KMT2A-rearranged acuteleukemia

description: This dataset includes whole genome and whole exome sequencing of diagnostic, germline, and relapse samples of 21 infants and 12 children with relapsed ALL (n=21) or AML (n=12), and targeted deep sequencing of 258 samples taken during treatment for 30 infants or children with ALL or AML. All patients had an underlying rearrangement of the <i>KMT2A</i> geneWhole genome and exome sequencing was performed using Illumina TruSeq Nano protocol. Targeted sequencing libraries was prepared using Nextera XT DNA Sample Preparation Kit (Illumina) and sequenced on the Illumina MiSeq.
DOI: 10.17044/scilifelab.26067604
title: <b>Measuring plasma membrane fluidity using spectral imaging</b>

description: It contains data sets from the publication, with the details for citation provided in the README file.Please cite this item as:Pablo Carravilla, Luca Andronico, Jan Schlegel, Yagmur B. Urem, Ellen Sjule, Franziska Ragaller, Florian Weber, Cenk O. Gurdap, Yavuz Ascioglu, Taras Sych, Joseph Lorent, Erdinc SezginDOI: 10.17044/scilifelab.26067604It contains GP images, raw microscopy images, figures and excel sheets of the data.<b>Abstract</b>:Membrane fluidity is a crucial parameter for cellular physiology. Recent evidence suggests that it varies in cell types, states, and in diseases. As membrane fluidity is gradually becoming a new player in cell biology and biomedicine, it is essential to have reliable and quantitative ways to measure it in cells. In the last decade, there has been significant progress both in chemical probes and in imaging tools to make membrane fluidity measurements easier and more reliable. We have recently established a robust pipeline, using spectral imaging and new environment-sensitive probes, which has been successfully used for several studies. In this protocol, we will present the key points of the membrane fluidity measurement from labelling to imaging and image analysis. The protocol takes around 4 hours and it requires basic expertise in cell culture, wet lab and microscopy.<b>Data usage</b>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.<b>Data Content</b>Excel files for graphslsm files for raw microscopy imagestif files for processed microscopy imagessvg files for figures<br><b>Software to open files</b>.csv - Microsoft Excel.lsm, .tif - Fiji (https://imagej.net/Fiji.html#Downloads).svg - Inkscape<br>
DOI: 10.17632/w693mpfcbm.1
title: Precursors of Interactional Value Formation through Digital Banking Interactions

description: Survey Questionnaire
DOI: 10.7483/opendata.cms.bbbj.d27c
title: /TprimeTprime_M-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.26067604.v1
title: <b>Measuring plasma membrane fluidity using spectral imaging</b>

description: It contains data sets from the publication, with the details for citation provided in the README file.Please cite this item as:Pablo Carravilla, Luca Andronico, Jan Schlegel, Yagmur B. Urem, Ellen Sjule, Franziska Ragaller, Florian Weber, Cenk O. Gurdap, Yavuz Ascioglu, Taras Sych, Joseph Lorent, Erdinc SezginDOI: 10.17044/scilifelab.26067604It contains GP images, raw microscopy images, figures and excel sheets of the data.<b>Abstract</b>:Membrane fluidity is a crucial parameter for cellular physiology. Recent evidence suggests that it varies in cell types, states, and in diseases. As membrane fluidity is gradually becoming a new player in cell biology and biomedicine, it is essential to have reliable and quantitative ways to measure it in cells. In the last decade, there has been significant progress both in chemical probes and in imaging tools to make membrane fluidity measurements easier and more reliable. We have recently established a robust pipeline, using spectral imaging and new environment-sensitive probes, which has been successfully used for several studies. In this protocol, we will present the key points of the membrane fluidity measurement from labelling to imaging and image analysis. The protocol takes around 4 hours and it requires basic expertise in cell culture, wet lab and microscopy.<b>Data usage</b>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.<b>Data Content</b>Excel files for graphslsm files for raw microscopy imagestif files for processed microscopy imagessvg files for figures<br><b>Software to open files</b>.csv - Microsoft Excel.lsm, .tif - Fiji (https://imagej.net/Fiji.html#Downloads).svg - Inkscape<br>
DOI: 10.17632/w693mpfcbm.2
title: Precursors of Interactional Value Formation through Digital Banking Interactions

description: The data shows the research questionnaire used for the customers to collect the information on their perceptions related to digital banking interactions. The data was collected through physical and online mode from the digitally active banking cutsomers.
It also has the file that contains raw customer data collected through the survey.
DOI: 10.17044/scilifelab.26096371
title: Ex-vivo Drug Responses and Molecular Genetic Profiles of 597 Pediatric ALL Patients

description: This dataset contains genome-wide DNA methylation data generated from 55 pediatric acute lymphoblastic leukemia (ALL) samples originating from bone marrow or peripheral blood samples taken at ALL diagnosis (n = 55).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Per sample, 250 ng of genomic DNA was bisulfite converted using EZ DNA MethylationTM Kit from Zymo Research. The bisulfite converted DNA was eluted in 15μl according to the manufacturer´s protocol, evaporated to a volume of &lt;4μl, and used for methylation analysis using the Illumina Methylation EPICv2 array 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 Genome Studio (V2011.1). Peak-based correction was used to normalize the beta-value matrix. The following data will be made available upon request: i) the raw IDAT files, ii) a table with the unprocessed beta-values, signal intensities and detection p-values, iii) the processed beta-value matrix (peak-based corrected), iv) the CpG site annotation file and v) limited clinical information.<br><b>Terms for access</b>The 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.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.
DOI: 10.7483/opendata.cms.37sl.lq6w
title: /TprimeTprime_M-1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.26096371.v1
title: Ex-vivo Drug Responses and Molecular Genetic Profiles of 597 Pediatric ALL Patients

description: This dataset contains genome-wide DNA methylation data generated from 55 pediatric acute lymphoblastic leukemia (ALL) samples originating from bone marrow or peripheral blood samples taken at ALL diagnosis (n = 55).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Per sample, 250 ng of genomic DNA was bisulfite converted using EZ DNA MethylationTM Kit from Zymo Research. The bisulfite converted DNA was eluted in 15μl according to the manufacturer´s protocol, evaporated to a volume of &lt;4μl, and used for methylation analysis using the Illumina Methylation EPICv2 array 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 Genome Studio (V2011.1). Peak-based correction was used to normalize the beta-value matrix. The following data will be made available upon request: i) the raw IDAT files, ii) a table with the unprocessed beta-values, signal intensities and detection p-values, iii) the processed beta-value matrix (peak-based corrected), iv) the CpG site annotation file and v) limited clinical information.<br><b>Terms for access</b>The 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.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.
DOI: 10.17632/mg73b2wkr2.1
title: Supplementary data for the Ph.D. thesis "Experimental study of the origin of mass-independent isotopic fractionations in cosmochemistry" for the Hiroshima glasses (chapter V)

description: These different files are the supplementary data not added to Chapter V of the Ph.D. thesis "Experimental study of the origin of mass-independent isotopic fractionations in cosmochemistry" and of the article: "Condensation of the fallout glasses in the Hiroshima nuclear fireball resulting in oxygen Mass-independent Fractionation". The supplementary data (S7) are backscattered electron and optical images of the Hiroshima glasses. The two table files present the chemical, triple oxygen and silicon isotope compositions for Hiroshima glasses. The chemical data file (ST1) shows the chemical composition of 94 Hiroshima glasses. This data was obtained by analyses with a Zeiss-EVO Scanning Electron Microscope (IPGP-Paris), a CAMECA SX-100 electron microprobe (Camparis-Paris), and a JXA-8530F field emission electron microprobe (Natural History Museum of Vienna). The oxygen and silicon triple isotope composition data (ST2) presents the result of analyses realized with the CAMECA IMS 1270 E7 ion microprobe (CNRS-CRPG, Nancy) on 20 Hiroshima glasses.
DOI: 10.17632/4rzyjrrczv.1
title: Dirofilaria spp. in cats from the Mediterranean basin

description: Raw dataset of the project titled "Dirofilaria spp. infection in cats from the Mediterranean basin: diagnosis and epidemiology"
DOI: 10.7483/opendata.cms.i8cz.aeow
title: /TprimeTprime_M-1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17632/9kxw6mjzr9.1
title: Cortex 2024

description: As part of the Cortex Journal submission process, data in this repository has the intention to provide the necessary tools for reproduction of results.
DOI: 10.17632/9kxw6mjzr9.2
title: Cortex 2024

description: As part of the Cortex Journal submission process, data in this repository has the intention to provide the necessary tools for reproduction of results.