cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17044/scilifelab.25593405
title: DNAseq of S. coelicolor foraging sectors

description: S. coelicolor was inoculated on FAM and incubated for 20 days at 30°C, to allow for sectors to form. the sectors were isolated and grown in TSB media and 600 mg pellet was collected and sent to MicrobesNG for DNA preparation and Illumina sequencing. Trimmed (Trimmomatic) and quality checked reads (using MicrobesNG in-house scripts combined with Samtools BedTools, and bwa-mem) were returned for (i) inoculum "WTparental", (ii) first level sector "alpha"/"A0", (iii) second levels sectors "I"/"A1" and "IV"/"A4".
DOI: 10.7483/opendata.cms.rrxf.dxkc
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.25593405.v1
title: DNAseq of S. coelicolor foraging sectors

description: S. coelicolor was inoculated on FAM and incubated for 20 days at 30°C, to allow for sectors to form. the sectors were isolated and grown in TSB media and 600 mg pellet was collected and sent to MicrobesNG for DNA preparation and Illumina sequencing. Trimmed (Trimmomatic) and quality checked reads (using MicrobesNG in-house scripts combined with Samtools BedTools, and bwa-mem) were returned for (i) inoculum "WTparental", (ii) first level sector "alpha"/"A0", (iii) second levels sectors "I"/"A1" and "IV"/"A4".
DOI: 10.17044/scilifelab.25593480
title: GCMS agar samples from plates with S. coelicolor sampled every 3rd day for 27 days

description: For the nutrient analysis of agar, six replicates of agar plugs were collected from the edge of the dish and 0.5 cm away from the <i>S. coelicolor</i> colonies on 0.5xTSA medium. This sampling was performed on six different ⌀ 4 cm plates. Three control samples were taken at every time point from three sterile 0.5xTSA dishes. Sample preparation was performed according to A <i>et al.</i> (A et al., 2005). The samples were stored at -80 °C until analysis. Small aliquots of the remaining supernatants were pooled and used to create quality control (QC) samples. The samples were analyzed in batches according to a randomized run order on GC-MS. Derivatization and GCMS analysis were performed as described previously A <i>et al</i>. (A et al., 2005). Non-processed MS-files from the metabolic analysis were exported from the ChromaTOF software in NetCDF format to MATLAB-R2020a (Mathworks, Natick, MA, USA), where all data pre-treatment procedures (base-line correction, chromatogram alignment, data compression and Multivariate Curve Resolution) were performed. The extracted mass spectra were identified by comparisons of their retention index and mass spectra with libraries of retention time indices and mass spectra (Schauer et al., 2005). Mass spectra and retention index comparison was performed using NIST MS 2.2 software. Annotation of mass spectra was based on reverse and forward searches in the library. Masses and ratio between masses indicative of a derivatized metabolite were especially notified. The mass spectrum with the highest probability indicative of a metabolite and the retention index between the sample and library for the suggested metabolite was ± 5 the deconvoluted “peak” was annotated as an identification of a metabolite.
DOI: 10.17044/scilifelab.25593480.v1
title: GCMS agar samples from plates with S. coelicolor sampled every 3rd day for 27 days

description: For the nutrient analysis of agar, six replicates of agar plugs were collected from the edge of the dish and 0.5 cm away from the <i>S. coelicolor</i> colonies on 0.5xTSA medium. This sampling was performed on six different ⌀ 4 cm plates. Three control samples were taken at every time point from three sterile 0.5xTSA dishes. Sample preparation was performed according to A <i>et al.</i> (A et al., 2005). The samples were stored at -80 °C until analysis. Small aliquots of the remaining supernatants were pooled and used to create quality control (QC) samples. The samples were analyzed in batches according to a randomized run order on GC-MS. Derivatization and GCMS analysis were performed as described previously A <i>et al</i>. (A et al., 2005). Non-processed MS-files from the metabolic analysis were exported from the ChromaTOF software in NetCDF format to MATLAB-R2020a (Mathworks, Natick, MA, USA), where all data pre-treatment procedures (base-line correction, chromatogram alignment, data compression and Multivariate Curve Resolution) were performed. The extracted mass spectra were identified by comparisons of their retention index and mass spectra with libraries of retention time indices and mass spectra (Schauer et al., 2005). Mass spectra and retention index comparison was performed using NIST MS 2.2 software. Annotation of mass spectra was based on reverse and forward searches in the library. Masses and ratio between masses indicative of a derivatized metabolite were especially notified. The mass spectrum with the highest probability indicative of a metabolite and the retention index between the sample and library for the suggested metabolite was ± 5 the deconvoluted “peak” was annotated as an identification of a metabolite.
DOI: 10.7483/opendata.cms.ocnz.8soi
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH450_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.25593528
title: Untargeted metabolomics analysis of metabolites from foraging S. coelicolor

description: For untargeted metabolite analysis, agar stubs were punched out from just outside of <i>S. coelicolor</i> colonies on FAM and FAM+G. For each condition, twelve samples were isolated from plates inoculated with <i>S. coelicolor</i>, in addition to two control samples each from sterile plates. The weight of the samples were adjusted to 100 mg before the addition of 1 mL extraction buffer (90/10 v/v methanol:water). Internal standards (13C9-Phenylalanine, 13C3-Caffeine was, D4-Cholic acid, D8-Arachidonic Acid, 13C9-Caffeic Acid) were added to each sample. The sample was shaken at 30 Hz for 3 min in a mixer mill, and proteins were precipitated at +4 °C for 2h on ice. The sample was centrifuged at +4 °C, 14 000 rpm, for 10 min. The supernatant was transferred to a microvial and solvents evaporated. Before analysis, the sample was re-suspended in 10 + 10 µL methanol and water. The set of samples were analyzed in positive mode and negative mode. The chromatographic separation was performed on an Agilent 1290 Infinity UHPLC-system (Agilent Technologies, Waldbronn, Germany). Re-suspended aliquots (2 µL) of the agar extracts were injected onto an Acquity UPLC HSS T3, 2.1 x 50 mm, 1.8 µm C18 column in combination with a 2.1 mm x 5 mm, 1.8 µm VanGuard precolumn (Waters Corporation, Milford, MA, USA) held at 40°C. The gradient elution buffers were A (H2O, 0.1 % formic acid) and B (75/25 acetonitrile:2-propanol, 0.1 % formic acid). The compounds were detected with an Agilent 6550 Q-TOF mass spectrometer equipped with a jet stream electrospray ion source. The settings were kept identical between the modes, except the capillary voltage. The data processing was performed using the Recursive Feature Extraction algorithm within Agilent Masshunter Profinder version B.08.00 (Agilent Technologies Inc., Santa Clara, CA, USA). All multivariate statistical investigations (PCA) were performed using the software package SIMCA®-P+ version 15.0.2 (Umetrics, Umeå, Sweden).
DOI: 10.17044/scilifelab.25593528.v1
title: Untargeted metabolomics analysis of metabolites from foraging S. coelicolor

description: For untargeted metabolite analysis, agar stubs were punched out from just outside of <i>S. coelicolor</i> colonies on FAM and FAM+G. For each condition, twelve samples were isolated from plates inoculated with <i>S. coelicolor</i>, in addition to two control samples each from sterile plates. The weight of the samples were adjusted to 100 mg before the addition of 1 mL extraction buffer (90/10 v/v methanol:water). Internal standards (13C9-Phenylalanine, 13C3-Caffeine was, D4-Cholic acid, D8-Arachidonic Acid, 13C9-Caffeic Acid) were added to each sample. The sample was shaken at 30 Hz for 3 min in a mixer mill, and proteins were precipitated at +4 °C for 2h on ice. The sample was centrifuged at +4 °C, 14 000 rpm, for 10 min. The supernatant was transferred to a microvial and solvents evaporated. Before analysis, the sample was re-suspended in 10 + 10 µL methanol and water. The set of samples were analyzed in positive mode and negative mode. The chromatographic separation was performed on an Agilent 1290 Infinity UHPLC-system (Agilent Technologies, Waldbronn, Germany). Re-suspended aliquots (2 µL) of the agar extracts were injected onto an Acquity UPLC HSS T3, 2.1 x 50 mm, 1.8 µm C18 column in combination with a 2.1 mm x 5 mm, 1.8 µm VanGuard precolumn (Waters Corporation, Milford, MA, USA) held at 40°C. The gradient elution buffers were A (H2O, 0.1 % formic acid) and B (75/25 acetonitrile:2-propanol, 0.1 % formic acid). The compounds were detected with an Agilent 6550 Q-TOF mass spectrometer equipped with a jet stream electrospray ion source. The settings were kept identical between the modes, except the capillary voltage. The data processing was performed using the Recursive Feature Extraction algorithm within Agilent Masshunter Profinder version B.08.00 (Agilent Technologies Inc., Santa Clara, CA, USA). All multivariate statistical investigations (PCA) were performed using the software package SIMCA®-P+ version 15.0.2 (Umetrics, Umeå, Sweden).
DOI: 10.7483/opendata.cms.4hgv.8vs5
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.wy6e.8z2t
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH500_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.r9y3su
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Isomira testacea Seidlitz, 1896",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 102 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071224-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17044/scilifelab.25705512
title: <b>Massively parallel analysis of single-molecule dynamics on next generation sequencing chips</b>

description: Data and codes underlying the findings of the manuscript "Massively parallel analysis of single-molecule dynamics on next generation sequencing chips". Please see the Materials and Methods section of the manuscript for the description of experimental procedures. For every figure in the paper a README file is provided, listing the datasets used for the figure, as well as the MATLAB analysis codes used to generate the respective plots. The latest version of the Python codes for matching single-molecule FRET traces with sequenced clusters is available at https://github.com/deindllab/MUSCLE/.The abstract of the manuscript: "Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe MUltiplexed Single-molecule Characterization at the Library scalE (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profile the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding/rewinding dynamics. For Cas9, the ability to explore a large sequence space allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes."
DOI: 10.17044/scilifelab.25705512.v1
title: <b>Massively parallel analysis of single-molecule dynamics on next generation sequencing chips</b>

description: Data and codes underlying the findings of the manuscript "Massively parallel analysis of single-molecule dynamics on next generation sequencing chips". Please see the Materials and Methods section of the manuscript for the description of experimental procedures. For every figure in the paper a README file is provided, listing the datasets used for the figure, as well as the MATLAB analysis codes used to generate the respective plots. The latest version of the Python codes for matching single-molecule FRET traces with sequenced clusters is available at https://github.com/deindllab/MUSCLE/.The abstract of the manuscript: "Single-molecule techniques are ideally poised to characterize complex dynamics but are typically limited to investigating a small number of different samples. However, a large sequence or chemical space often needs to be explored to derive a comprehensive understanding of complex biological processes. Here we describe MUltiplexed Single-molecule Characterization at the Library scalE (MUSCLE), a method that combines single-molecule fluorescence microscopy with next-generation sequencing to enable highly multiplexed observations of complex dynamics. We comprehensively profile the sequence dependence of DNA hairpin properties and Cas9-induced target DNA unwinding/rewinding dynamics. For Cas9, the ability to explore a large sequence space allowed us to identify a number of target sequences with unexpected behaviors. We envision that MUSCLE will enable the mechanistic exploration of many fundamental biological processes."
DOI: 10.7483/opendata.cms.9se7.gyq8
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.25836238
title: Allel-specific read counts for sprat population samples used in "Limited parallelism in genetic adaptation to brackish water bodies in European sprat and Atlantic herring"

description: "Sprat_DeDup_v2_HiC.fasta.gz" contians the draft genome used in the study, in FASTA format. It is the version upon the SNPs (single nucleotide polymorphisms) described have been called.
"Sprat_pool_AD_DeDup_v2_PASS.txt.gz" contains tab-separated allele counts, as output using the "--counts" option in vcftools, from pool re-sequencting of the samples used in the study. Please see the corresponding supplementary information for sample codes etc. These counts underly the frequencies used throughout the study.
DOI: 10.17044/scilifelab.25836238.v1
title: Allel-specific read counts for sprat population samples used in "Limited parallelism in genetic adaptation to brackish water bodies in European sprat and Atlantic herring"

description: "Sprat_DeDup_v2_HiC.fasta.gz" contians the draft genome used in the study, in FASTA format. It is the version upon the SNPs (single nucleotide polymorphisms) described have been called.
"Sprat_pool_AD_DeDup_v2_PASS.txt.gz" contains tab-separated allele counts, as output using the "--counts" option in vcftools, from pool re-sequencting of the samples used in the study. Please see the corresponding supplementary information for sample codes etc. These counts underly the frequencies used throughout the study.
DOI: 10.7483/opendata.cms.fx6e.cxip
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH50_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.uo73.30my
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.25883455.v1
title: Manually annotated IFCB plankton images from the Skagerrak, Kattegat, and Baltic Proper by SMHI

description: This repository includes three datasets of manually annotated plankton images by phytoplankton experts at the Swedish Meteorological and Hydrological Institute (SMHI). These images can be used for training classifiers to identify various plankton species. The images were captured using an Imaging FlowCytobot (IFCB, McLane Research Laboratories) from different locations and seasons in the Skagerrak, Kattegat, and Baltic Proper. The specifics of the three datasets are as follows:<br><b>smhi_ifcb_svea_baltic_proper</b>: Images were gathered during monthly monitoring cruises from 2022 to 2024, utilizing an IFCB mounted as part of the underway FerryBox system on the R/V Svea. This collection consists of 18,421 annotated images across 48 different classes.<b>smhi_</b><b>ifcb_svea_skagerrak_kattegat</b>: Images were collected during regular monitoring cruises from 2022 to 2024. This archive comprises of 5,086 annotated images from 83 distinct classes.<b>smhi_</b><b>ifcb_tångesund</b>: In 2016, the IFCB was deployed in situ at depths between 3 and 18 meters, near a mussel farm in Tångesund, Mollösund. This dataset contains 41,928 annotated images from 27 different classes.Each dataset comprises two zip archives: one (annotated_images) containing .png images organized into subfolders for each class, and another (matlab_files) including raw data files (.roi, .hdr, .adc) and .mat-files for developing an image classifier using the MATLAB code available at https://github.com/hsosik/ifcb-analysis.The images in this dataset undergo continuous quality control, and new images are regularly added. Consequently, this dataset will be updated on a regular basis.
DOI: 10.17044/scilifelab.25883455.v2
title: SMHI IFCB plankton image reference library

description: This repository includes three datasets of manually annotated plankton images by phytoplankton experts at the Swedish Meteorological and Hydrological Institute (SMHI). These images can be used for training automatic image classifiers to identify various plankton species. The images were captured using an Imaging FlowCytobot (IFCB, McLane Research Laboratories) from different locations and seasons in the Skagerrak, Kattegat, and Baltic Proper. The specifics of the three datasets are as follows:<br><b>smhi_ifcb_svea_baltic_proper</b>: Images were gathered during monthly monitoring cruises from 2022 to 2024, utilizing an IFCB mounted as part of the underway FerryBox system on the R/V Svea. This collection consists of 22,805 annotated images across 57 different classes.<b>smhi_</b><b>ifcb_svea_skagerrak_kattegat</b>: Images were also collected during the regular monitoring cruises from 2022 to 2024. This archive comprises of 5,086 annotated images from 83 distinct classes.<b>smhi_</b><b>ifcb_tångesund</b>: In 2016, the IFCB was deployed in situ at depths between 3 and 18 meters, near a mussel farm in Tångesund, Mollösund (Skagerrak). This dataset contains 43,634 annotated images from 33 different classes.Each dataset comprises two zip archives: one (annotated_images) containing .png images organized into subfolders for each class, and another (matlab_files) including raw data files (.roi, .hdr, .adc) and .mat-files for developing a random forest image classifier using the MATLAB code available at https://github.com/hsosik/ifcb-analysis.The images in this dataset undergo continuous quality control, and new images are regularly added. Consequently, this dataset will be updated on a regular basis. If you find any mislabeled images, please contact the authors.<b>Version history</b>Version 2 (2024-06-03): 71,525 annotated images. Updated class names and corrected manual files in the Tångesund dataset. Continued quality control of images in the Tångesund dataset.Version 1 (2024-05-31): 65,435 annotated images
DOI: 10.7483/opendata.cms.xb13.j0sw
title: /TprimeBToTH_TLep_Hbb_LH_MT900_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.25883455.v3
title: SMHI IFCB plankton image reference library

description: This repository includes four datasets of manually annotated plankton images by phytoplankton experts at the Swedish Meteorological and Hydrological Institute (SMHI). These images can be used for training automatic image classifiers to identify various plankton species. The images were captured using an Imaging FlowCytobot (IFCB, McLane Research Laboratories) from different locations and seasons in the Skagerrak, Kattegat, and Baltic Proper. The specifics of the three datasets are as follows:<br><b>smhi_ifcb_svea_baltic_proper</b>: Images were gathered during monthly monitoring cruises from 2022 to 2024, utilizing an IFCB mounted as part of the underway FerryBox system on the R/V Svea. This collection consists of 23,366 annotated images across 57 different classes.<b>smhi_</b><b>ifcb_svea_skagerrak_kattegat</b>: Images were also collected during the regular monitoring cruises from 2022 to 2024. This archive comprises of 5,086 annotated images from 83 distinct classes.<b>smhi_</b><b>ifcb_tångesund</b>: In 2016, the IFCB was deployed in situ at depths between 3 and 18 meters, near a mussel farm in Tångesund, Mollösund (Skagerrak). This dataset contains 43,634 annotated images from 33 different classes.<b>smhi_</b><b>ifcb_iRfcb</b>: This subset of the smhi_ifcb_svea_skagerrak_kattegat dataset can be used for user and unit tests for the iRfcb R package.Datasets 1-3 comprises two zip archives: one (annotated_images) containing .png images organized into subfolders for each class, and another (matlab_files) including raw data files (.roi, .hdr, .adc) and .mat-files for developing a random forest image classifier using the MATLAB code from the ifcb-analysis repository. Dataset 4 only comprise of a MATLAB data package.The images in this dataset undergo continuous quality control, and new images are regularly added. Consequently, this dataset will be updated on a regular basis. If you find any mislabeled images, please contact the authors.<b>Version history</b>Version 3 (2024-08-05): 72 086 annotated images. Added iRfcb dataset for user and unit testing.Version 2 (2024-06-03): 71,525 annotated images. Updated class names and corrected manual files in the Tångesund dataset. Continued quality control of images in the Tångesund dataset.Version 1 (2024-05-31): 65,435 annotated images<br>