Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Characterizing Metabolic Alterations in Early-stage chronic kidney disease (CKD) patients: A Pathway for Improved Diagnosis and Personalized Treatment.

The raw NMR data that I have uploaded contains the final concentration results that have been used for this study.

opencc-by-4.0Jul 2021View details →

E/Z Switchable Ring-closing Metathesis in 1,1′-Bis(but-3-enyl)ferrocenes: Synthesis and Characterization of Axially Chiral ansa[6]-Ferrocenes. Raw Diffraction Data.

Diffraction data for article E/Z Switchable Ring-Closing Metathesis in 1,1′-Bis(but-3-enyl)ferrocenes: Synthesis and Characterization of Axially Chiral ansa[6]-Ferrocenes doi: <a href="https://doi.org/10.1021/acs.organomet.2c00163">10.1021/acs.organomet.2c00163</a>. The crystal structures have been deposited in CSD with CCDC numbers 2093772-2093775, 2094024-2094027 and 2119739.

opencc-zeroOct 2021View details →

Data for Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach

Environmental DNA (eDNA) is gaining a growing popularity among scientists but its applicability to biodiversity research and management remains limited in river systems by the lack of knowledge about the spatial extent of the downstream transport of eDNA. Here, we assessed the ability of eDNA inventories to retrieve spatial patterns of fish assemblages along two large and species rich Neotropical rivers. We first examined overall community variation with distance through the distance decay of similarity and compared this pattern to capture-based samples. We then considered previous knowledge on individual species distributions, and compared it to the eDNA inventories for a set of 53 species. eDNA collected from 28 sites in the Maroni and 25 sites in the Oyapock rivers permitted to retrieve a decline of species similarity with distance between sites. The distance decay of similarity derived from eDNA was similar, and even more pronounced, than that obtained with capture-based methods (gil-nets). In addition, the species upstream-downstream distribution range derived from eDNA matched to the known distribution of most species. Our results demonstrate that environmental DNA does not represent an integrative measure of biodiversity across the whole upstream river basin but provide a relevant picture of local fish assemblages. Importantly, the spatial signal gathered from eDNA was therefore comparable to that gathered with local capture based methods, which describes fish fauna over a few hundred metres.

opencc-by-4.0Oct 2021View details →

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)

opencc-by-4.0Nov 2021View details →

Dataset for TLS understory characterization: an exploratory study on hazel grouse in Italian Alps

This dataset contains data collected in the middle of June 2021 with a mobile terrestrial laser scanner (mobile ZEB TLS) in 10 squared areas, of approximatively 20x20m each, in Adamello Brenta National Park. Data have been normalized using TreeLS package in R. This research was funded with the contribution of the Italian Ministry of Agricultural, Food, and Forestry Policies (MiPAAF) sub-project “Precision Forestry” (AgriDigit program) (DM 36503.7305.2018 of 20/12/2018).

opencc-by-nc-sa-4.0Nov 2021View details →

Detection and characterization of small-sized microplastics (≥ 5 µm) in milk products

Data used in the scientific article to be published in Nature Scientific Reports.

opencc-zeroDec 2021View details →

Data for genetic characterization and curation of diploid a-genome wheat species

Diploid A-genome relatives of wheat comprises T. urartu, T. monococcum subsp. monococcum (domesticated einkorn) and T. monococcum subsp. aegilopoides (wild einkorn). About 930 accessions of A-genome diploid wheat species preserved in the gene bank of the Wheat Genetics Resource Center (WGRC) at Kansas State University were genotyped using genotyping-by-sequencing (GBS). We constructed four pooled GBS libraries (384- and 288-plex) using restriction enzymes (Pst1-Msp1) combinations and the libraries were sequenced on the Illumina platform. The sequence data was processed using Tassel 5 GBS v2 pipeline and identified thousands of single nucleotide polymorphisms (SNPs) for downstream genetic and genomic dissections of the tested population. We have used T. urartu pseudomolecule (Tu2.0) as a reference genome to detect the genetic markers. Four fastq files with raw sequence reads information can be obtained at the National Center for Biotechnology Information (NCBI) SRA database with the BioProject accession PRJNA744683 (<a href="https://www.ncbi.nlm.nih.gov/sra/PRJNA744683" rel="noopener noreferrer">https://www.ncbi.nlm.nih.gov/sra/PRJNA744683</a>). Provided key file has information for demultiplexing including flowcell, lane number, barcodes and sample names to replicate the analysis. This experiment led us to curate the gene bank through identification of genetically duplicated accessions, miss-classified accessions and miss-classified non-diploid accessions. We were able to observe the unique genetic and evolutionary relationships among the diploid A-genome wheat species along with the unique population structures per species and sub-species.

Laser light sources for Photobiomodulation: The role of power and beam characterization in treatment accuracy and reliability

Purpose: Daily clinical use of therapeutic light sources can lead to changes in light emission stability with potentially significant consequences for usage in photomedicine treatment. The aim of this study was to evaluate the average and maximum power and to describe the beam diameter of the low-power lasers used in clinical photobiomodulation. Methods: The power and light-emitting beam diameter of twenty-four therapeutic devices with an average age of 11±5 years, with an average weekly use of fewer than thirty minutes, were measured. Results: The analyzed power varied between 2% to 134% of the values declared by the manufacturers. Differences in beam diameter of between 38% and 543% of the nominal values were also observed. It is also noteworthy that even between the same brand and model, differences in diameter were obtained. Finally, differences were observed in the power output after one and three minutes of sequential emission for 830 nm and 904 nm (p < 0.05), but not when comparing the difference between wavelengths in factor time. Conclusion: There is a need for a shared effort on the part of laser manufacturers to improve standardization and consistency of laser output power and beam diameters. At the same time, medical laser operators should also consider development of standardized protocols for maintenance and monitoring equipment performance over time to correct for fluctuations that could ultimately impact on treatment outcomes.

Raw recordings for electrophysiological characterization of CNO-DREADD mediated responses in noradrenergic locus coeruleus (LC) neurons from lines RR1(P), RR2(P), and RC::FPDi (P).

Whole-cell recordings were performed as described previously by the Jiang lab. Briefly, patch pipettes (2-7 MΩ) were filled with an internal solution containing 120 mM potassium gluconate, 10 mM HEPES, 4 mM KCl, 4 mM MgATP, 0.3 mM Na3GTP, 10 mM sodium phosphocreatine and 0.5% biocytin (pH 7.25). Whole-cell recordings from up to 8 LC neurons were performed using two Quadro EPC 10 amplifiers (HEKA Electronic, Germany). PatchMaster (HEKA) and custom-written Matlab-based programs (Mathworks) were used to operate the recording system and perform online and offline data analysis. In current-clamp recordings, neurons were first current clamped at ~-40pA to prevent spontaneous firing. Prior to investigating the effect of drugs, we calculated spike thresholds and recorded firing patterns in response to sustained depolarizing currents by injecting increasing current steps (+10pA). Continuous recordings were obtained from LC neurons current clamped at -40 to 0 pA during drug wash-on experiments. We also calculated other intrinsic electrophysiological parameters, such as the input resistance, membrane time constant, spike amplitude, after-hyperpolarization (AHP) etc.

opencc-by-4.0Dec 2020View details →

Circuit-QED characterization of a topological Josephson junction

The search for topological superconductivity in topological insulator (TI) nanowires have attracted a lot of interest due to potential applications in the field of topologically protected quantum computation [1-3]. One route to emulate unconventional superconductivity is to build a topological Josephson junction from a TI nanowire (Bi2Se3) connected to two conventional superconducting electrodes (Al). Such topological Josephson junctions are expected to host Majorana zero-energy modes (bound states) when they are phase-biased at pi. The bound state spectrum of a junction hosting several transport modes consists of topological trivial Andreev bound states and topologically protected Majorana bound states. The phase dependence of those bound states has been studied experimentally using a circuit-QED-like setup, where the topological junction is embedded in a superconducting resonator. Here the frequency response of the coupled resonator/junction system to an externally applied magnetic field (phase bias) at various temperatures is used to deduce information about the phase dependence of the bound state spectrum of the junction. I detail, the contributions to the junction dissipation (which is directly reflected in the inverse quality factor of the coupled resonator/junction system) originating from zero-energy bound states and topological trivial Andreev bound states are rather distinct, which is mainly reflected in their phase bias dependence around and their evolution in temperature.

Characterization of Pore Structure with Box Counting Fractal Dimension Based on Digital Rock

This is a supplementary data set for a manuscript submitted to Journal of Geophysical Research: Solid Earth. This data set includes CT samples, process-based model, fractal dimensions calculated by the box counting algorithm, and Matlab codes to implement these modeling and fractal calculations.

opencc-zeroDec 2021View details →

Geochemical and physical characterization of lithic raw materials in the Olduvai Basin, Tanzania

The invention and proliferation of stone tool technology in the Early Stone Age (ESA) marks a watershed in human evolution. Patterns of lithic procurement, manufacture, use, and discard have much to tell us about ESA hominin cognition and land use. However, these issues cannot be fully explored outside the context of the physical attributes and spatio-temporal availability of the lithic raw materials themselves. The Olduvai Basin of northern Tanzania, which is home to both a wide variety of potential toolstones and a rich collection of ESA archaeological sites, provides an excellent opportunity to investigate the relationship between lithic technology and raw material characteristics. Here, we examine two attributes of the basin's igneous and metamorphic rocks: spatial location and fracture predictability. A total of 244 geological specimens were analyzed with non-destructive portable XRF (pXRF) to determine the geochemical distinctiveness of five primary and secondary sources, while 110 geological specimens were subjected to Schmidt rebound hardness tests to measure fracture predictability. Element concentrations derived via pXRF show significant differences between sources, and multivariate predictive models classify geological specimens with 75–80% accuracy. The predictive models identify Naibor Soit as the most likely source for a small sample of three lithic artifacts from Bed II, which supports the idea that this inselberg served as a source of toolstone during the early Pleistocene. Clear patterns in fracture predictability exist within and between both sources and rock types. Fine-grained volcanics show high rebound values (associated with high fracture predictability), while finer-grained metamorphics and coarsegrained gneisses show intermediate and low rebound values, respectively. Artifact data from Bed I and II suggest that fracture predictability played a role in raw material selection at some sites, but other attributes like durability, expediency, and nodule size and shape were more significant.

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. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s

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. Immgen_cell_types.cls : Microarray Phase 1 expression 2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

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: cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis

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:  MDAlab_cDC1_maturation.tar : Docker image used for the analysis

opencc-zeroJan 2022View details →

Characterization and biodiversity of native Azotobacter in semi-arid agroecosystems of Eastern Kenya

Declining food production in the African agroecosystems is attributable to changes in weather patterns, soil infertility, and limited farming inputs. The exploitation of plant growth-promoting soil microbes could remedy these problems. Azotobacter, a free-living, nitrogen-fixing bacterium, confers stress tolerance, avails phytohormones, and aids in soil bioremediation. The study aimed to isolate, characterize and determine the biodiversity of native Azotobacter isolates from soils in semi-arid Eastern Kenya. The isolation was conducted on nitrogen-free Ashby's agar and the morphological, biochemical and molecular attributes were evaluated. The isolates were sequenced using DNA amplicons of 27F and 1492R primers of the 16SrRNA gene loci. The Basic Local alignment search Tool (BLASTn) analysis of their sequences, revealed the presence of three main Azotobacter species viz., Azotobacter vinelandii, Azotobacter salinestris, and Azotobacter tropicalis. Azotobacter vinelandii was the most dominant species. Kitui County had the highest number of recovered Azotobacter isolates (45.4%) with the lowest diversity index (0.8761). Tharaka Nithi County showed the lowest occurrence (26.36%) with a diversity index of (1.057). The diversity was influenced by the soil pH, texture, and total organic content. This study revealed the presence of native strains of Azotobacter species in Kenyan soils with the potential for utilization as a bioinoculant.

opencc-by-4.0Jan 2022View details →

TXRF and TXRF-XANES data for characterization of unique aerosol pollution episodes in urban areas

The dataset contains the raw data for elemental composition and copper/bromine speciation determined in size-fractionated aerosol particles sampled by a May-type cascade impactor.   These data are related to the journal article Characterization of unique aerosol pollution episodes in urban areas using TXRF and TXRF-XANES by Otto Czömpöly, Endre Börcsök, Veronika Groma, Simone Pollastri and Janos Osan, published in Atmospheric Pollution Research Volume 12, Issue 11, November 2021, 101214. <a href="https://doi.org/10.1016/j.apr.2021.101214">https://doi.org/10.1016/j.apr.2021.101214</a> The elemental composition data were produced by total-reflection X-ray Spectrometry (TXRF) at Centre for Energy Research, Budapest, Hungary.  The file "TXRF.zip" contains raw spectra as "*.spe", fitting results as "*.asr", calibration file "aer_mo.cal" and calculated elemental masses along the 20-mm stripe samples as "*.apr", all as text files in AXIL/QXAS format. The copper and bromine speciation data were produced by X-ray absorption near-edge structure (XANES) recorded in the TXRF detection mode at the XRF beamline of Elettra Sincrotrone Trieste, Italy. The file "XANES.zip" contains average XANES spectra of several energy scans for each sample as "*.xmu" and linear combination fitting results as "*.lcf", all as text files in Athena/Ifeffit format. The files are grouped in folders related to aerosol particles collected during the five pollution episodes (A-E) and near pollution sources as presented in the publication. The digit following the sample number denotes the impactor stage number (3-9), numbered from large (4.5-8.9 um) to small (70-180 nm) particle fractions.

opencc-by-4.0Jan 2022View details →

Database of vulnerability indicators used in the holistic characterization of vulnerability to flash floods in the region of Castilla y León (Spain)

Database containing the vulnerability indicators used in the holistic analysis of vulnerability to flash floods in the region of Castilla y León (Spain), considering all its dimensions (social, economic, ecosystem, physical, institutional and cultural) and components (exposure, susceptibility and resilience). The database contains a total of 496 variables, of which 216 characterize social vulnerability, 180 economic vulnerability, 49 ecosystem vulnerability, 22 physical vulnerability, 23 institutional vulnerability and 6 cultural heritage vulnerability. The Excel file contains two sheets for each vulnerability dimension. The first sheet (whose name is composed with the name of the dimension and the suffix '_Variables') contains the information of the variables, i.e., the names of the municipalities, the province to which they belong and the values of the variables for each municipality. The variables on this sheet are identified as codes, whose definition and description (unit in which they are expressed, reference year of the information, information source and the link to the information) are found on the second sheet of each dimension (suffix '_Data_sources').