Find research datasets worth reusing

opencc-by-4.0Feb 2011View details →

Fig. 2 in Karyotype characterization of Mugil incilis Hancock, 1830 (Mugiliformes: Mugilidae), including a description of an unusual co-localization of major and minor ribosomal genes in the family

Fig. 2. Metaphase plates of Mugil incilis after (a) C-banding and (b) AgNO -staining. Arrows indicate chromosome pair number 1.

Fig. 5 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species

Fig. 5. – Infructescences and details of stigmas of species of Benstonea Callm. & Buerki. A. Benstonea parva (Ridl.) Callm. & Buerki; B. Benstonea pectinata (Martelli) Callm. & Buerki; C. Benstonea rupestris (. C. Stone) Callm. & Buerki; D. Benstonea thomissophylla (. C. Stone) Callm. & Buerki. [Photos: M. W. Callmander]

Fig. 6 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species

Fig. 6. – Infructescence of Benstonea thurstonii (C. H. Wright) Callm. & Buerki with details of stigmas in frame. [Photo: M. W. Callmander]

Fig. 2 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species

Fig. 2. – Plastid maximum likelihood phylogenetic tree of Pandanaceae inferred using RAxML and based on matK, trnL-trnF and trnQ-rps16. Bootstrap support values are represented below branches. This figure is adapted from the figure S1 in BUERKI & al. (2012).

Fig. 1 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species

Fig. 1. – General habit, infructescences and details of stigmas of species of Pandanus sect. Epiphytica Martelli (A-B) and Pseudoacrostigma B. C. Stone (C-D). A-B. Pandanus epiphyticus Martelli; C. Pandanus platystigma Martelli; D. Pandanus pugnax B. C. Stone. [Photos: M. W. Callmander]

Fig. 3 in Benstonea Callm. & Buerki (Pandanaceae): characterization, circumscription, and distribution of a new genus of screw-pines, with a synopsis of accepted species

Fig. 3. – Distribution map of Benstonea Callm. & Buerki showing the number of species and the level of endemicity per geographical region.

opencc-by-4.0Dec 2021View details →

Data Repository for "Single-particle characterization of polycyclic aromatic hydrocarbons in background air in Northern Europe", Atmos. Chem. Phys.

Data Repository for  Passig et al., "Single-particle characterization of polycyclic aromatic hydrocarbons in background air in Northern Europe", Atmospheric Chemistry and Physics, 2021/22 Details in Readme.txt

opencc-by-4.0Dec 2021View details →

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis 2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

Molecular and biological characterization of an Asian-American isolate of Chikungunya Virus

This is a dataset of the figures used in the development of the manuscript Molecular and biological characterization of an Asian-American isolate of Chikungunya Virus

CUSP - UBC Workshop: Analytics: Characterization and quantification / enumeration of particles in the environment and in tissue

The CUSP-UBC Workshop was held online on 28th January 2022. 69 participants took part from across Europe and British Columbia to share experiences, exchange knowledge, and to discuss challenges and solutions as part of a great collaboration between the two clusters. Acknowledgements: Co-Organisation and Cluster Presentations: Lesley Tobin (CUSP Working Group 6 Communication and Dissemination, PlasticsFatE) <a href="mailto:lesley.tobin@optimat.co.uk"> </a><a href="mailto:lesley.tobin@optimat.co.uk">lesley.tobin@optimat.co.uk</a> Mahdi Takaffoli (Coordinator, Cluster for Microplastics, Health and the Environment, The University of British Columbia) <a href="mailto:mahdi.takaffoli@ubc.ca">mahdi.takaffoli@ubc.ca</a> Presenters: Florian Meirer (Associate Professor, Inorganic Chemistry and Catalysis research group at Utrecht University; Polyrisk & Aurora) “Characterizing Nanoplastics with Force Microscopy – An Update”) <a href="mailto:F.Meirer@uu.nl">F.Meirer@uu.nl</a> Anna Costa (Environmental Nanotechnology and Nano-Safety group of CNR-ISTEC; PlasticsFatE) “Strategies for MP/NP simulated samples-laboratory tests” <a href="mailto:anna.costa@istec.cnr.it">anna.costa@istec.cnr.it</a> Tao Huan (Assistant Professor, Chemistry, The University of British Columbia) “Pilot Study of the Impact of Microplastics on Cell Liability and Potential Application of Metabolomics in Understanding the Biological Mechanisms” <a href="mailto:thuan@chem.ubc.ca">thuan@chem.ubc.ca</a>  Edward Grant (Professor, UBC Chemistry) “The challenge of representative microplastic analysis” edgrant@chem.ubc.ca Thank you to Michelle Epstein, Doctor of Allergy and Clinical Immunology, MedUni Vienna, for such a useful, stimulating idea, and to everyone who took part despite the unsocial hours!

Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results

<pre> </pre> The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Companion Repositories: <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> Companion Datasets <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>.    The file jurkat.flnc.bam was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up.    1. Convert jurkat.flnc.bam (binary format) to sam file (text format) without header:  samtools view jurkat.flnc.bam > jurkat.flnc.sam 2. Capture the header: samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam 3. Split jurkat.flnc.sam into smaller files (aim to get final size under 2GB): split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk. 4. Convert each of these files back to bam for uploading: samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m) After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file> 1. Convert the bam files back to sam files: samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m) 2. Combine the header together with the sam files: cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761.  Header file is 13 lines. 3. Convert to bam files if desired: samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam 4. Rehead with the header file: samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam

opencc-by-4.0Nov 2013View details →

Fig. 5 in Floristic traits and biogeographic characterization of the Gennargentu massif (Sardinia)

Fig. 5. – Percentages of the chorologic units of the endemic flora of Gennargentu. EMOI = W-Mediterranean insular endemics; ETI = Tyrrhenian insular endemics; ET = Tyrrhenian endemics; ETI-NA = Tyrrhenian insular and N-Africa endemics; ESS = Sardinia and Sicily endemics; ESC = Sardo-Corsican endemics; ESA = Sardinian endemics.

opencc-by-4.0Nov 2013View details →

Fig. 2 in Floristic traits and biogeographic characterization of the Gennargentu massif (Sardinia)

Fig. 2. – Life forms percentages, referred to the whole flora. H = hemicryptophytes; C = chamaephytes; G = geophytes; NP = nanophanerophytes; P = phanerophytes; T = therophytes; Hy = hydrophytes.

opencc-by-4.0Nov 2013View details →

Fig. 4 in Floristic traits and biogeographic characterization of the Gennargentu massif (Sardinia)

Fig. 4. – Life forms percentages of the endemic flora of Gennargentu. H = hemicryptophytes; C = chamaephytes; G = geophytes; NP = nanophanerophytes; P = phanerophytes; T = therophytes.

opencc-by-4.0Mar 2021View details →

Data set for publication: Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves

This is dataset for paper published: Crotti, G.; D’Avanzo, G.; Giordano, D.; Letizia, P.S.; Luiso, M. Extended SINDICOMP: Characterizing MV Voltage Transformers with Sine Waves. Energies 2021, 14, 1715. https://doi.org/10.3390/en14061715   Excel file provides data for Table 2, Table 4 and Figure 10 a.

opencc-by-4.0Jul 2021View details →

Data set for publication: A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers

This is dataset for paper published: G. Crotti, D. Giordano, G. D'Avanzo, P.S. Letizia, M. Luiso, "A New Industry-Oriented Technique for the Wideband Characterization of Voltage Transformers", Measurement, Volume 182, 2021, 109674, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.109674.   Excel file provides data for Figure 3, 4, 6 and 7.

opencc-by-4.0Jan 2021View details →

Characterization of legacy persistent organic pollutants (POPs) in northern bottlenose whales of the Western North-Atlantic

Dataset associated with the Canadian Technical Report of Fisheries and Aquatic Sciences entitled "Characterization of legacy persistent organic pollutants (POPs) in northern bottlenose whales of the Western North-Atlantic". Presents lipid weight corrected persistent organic pollutant concentrations (microgram per gram) in the blubber of northern bottlenose whales samples in the western North Atlantic.

dryad40/100

Characterizing North American Ipomoea grandifolia (Convolvulaceae), a member of Ipomoea series batatas

Species in the genus Ipomoea are often difficult to identify due to their similar morphologies and their ability to hybridize with one another. An undescribed North American Ipomoea morphotype in series Batatas, referred here as Ipomoea Carolina morphotype, was found to be morphologically, genetically, and reproductively isolated from other locally co-occurring Ipomoea species. A previous phylogenetic analysis that included a broader sampling of species in Ipomoea series Batatas suggested that Ipomoea Carolina morphotype may be Ipomoea grandifolia, a species described as found only in South America. To evaluate these findings, we tested intrinsic cross-compatibility between Ipomoea Carolina morphotype and I. grandifolia as well as with three other co-localizing North American Ipomoea species – Ipomoea cordatotriloba, Ipomoea lacunosa, and Ipomoea leucantha. We also examined genetic differentiation using single nucleotide polymorphisms from leaf transcriptomes from multiple individuals of all five species and several outgroup species. We find no cross-incompatibility and little genetic differentiation between Ipomoea Carolina morphotype and Ipomoea grandifolia, suggesting that Ipomoea Carolina morphotype is a representative of Ipomoea grandifolia. This finding raises additional questions about the origins of Ipomoea grandifolia in North America and how its disjunct distribution could play a role in the divergence of Ipomoea grandifolia in the future.

opencc-zeroMar 2022View details →