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990 results for “quantification”
Quantification of gamma radiation exposure and radon/thoron exhalation rates in representative building materials in Ireland
<p>Data, models, mapsa nd publication produced during the postdoctoral fellowship: EPSPD/2022/141 </p>
Example raw quantification outputs used for extracting view data
This data contains the raw quantification outputs from platforms FragPipe, Maxquant, DIA-NN and Spectronaut. We use them as inputs for extracting view data serving as inputs to our newly designed multi-view proteomics framework.
Supporting data for 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator'
<p>Supporting data for the preprint 'Rapid quantification of methane in water with parts-per-billion sensitivity using a metal-organic framework-functionalized quartz crystal resonator' published at ChemRxiv (doi://10.26434/chemrxiv-2024-x62zz)</p> <p> </p>
Quantification of preferential flow in single fracture using electrical monitoring
Open the record for dataset details and reuse information.
Data from: Robust quantification of fish early life CO2 sensitivities via serial experimentation
Despite the remarkable expansion of laboratory studies, robust estimates of single species CO2 sensitivities remain largely elusive. We conducted a meta-analysis of 20 CO2 exposure experiments conducted over six years on offspring of wild Atlantic silversides (Menidia menidia) to robustly constrain CO2 effects on early life survival and growth. We conclude that early stages of this species are generally tolerant to CO2 levels of ~ 2,000 µatm, likely because they already experience these conditions on diel to seasonal time scales. Still, high CO2 conditions measurably reduced fitness in this species by significantly decreasing average embryo survival (-9%) and embryo + larval survival (-13%). Survival traits had much larger coefficients of variation (>30%) than larval length or growth (3-11%). CO2 sensitivities varied seasonally and were highest at the beginning and end of the species' spawning season (April-July), likely due to the combined effects of transgenerational plasticity and maternal provisioning. Our analyses suggest that serial experimentation is a powerful, yet underutilized tool for robustly estimating small but true CO2 effects in fish early life stages.
Analysis code and quantification for publication "The stress-sensing domain of activated IRE1α forms helical filaments in narrow ER membrane tubes"
<p>Analysis code and input/output files for all quantifications performed for publication entitled "The stress-sensing domain of activated IRE1α forms helical filaments in narrow ER membrane tubes." </p> <p>All questions on the analyses or code can be directed to han@walterlab.ucsf.edu</p>
Datasets created for 'Representation and quantification of change on spatiotemporal phenomena'
<p>Spatiotemporal datasets generated in the context of the Master's Thesis 'Representation and quantification of change on spatiotemporal phenomena', namely for testing the spatiotemporal feature eXtractor prototype.</p> <p>A total of 7 zip archives have been uploaded, each containing: </p> <ul> <li>dataset.json: file containing the dataset information, resulting of the conversion of Blender <em>obj</em> files into json</li> <li>animation.blend: blender file so that the user can visualize the transformations occurring in each dataset</li> <li>readme.txt: textual description of the transformations occurring in each dataset</li> </ul>
Data for "Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1"
<p>The domain file and surface data file that used to run ELMv1, and processed ISIMP2a runoff data that used in <a href="https://gmd.copernicus.org/preprints/gmd-2021-401/">https://gmd.copernicus.org/preprints/gmd-2021-401/</a></p> <p>ELM_runoff_parameter_post.nc contains the ELM runoff generation relevant parameter posteriors at a global half degree spatial resolution.</p>
ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data (repository for simulated ONT RNA-seq data)
<p>Simulated ONT direct RNA and 1D cDNA sequencing data of varying sequencing depths (0.5 million, 1 million, 3 million, and 5 million simulated reads) used for benchmark evaluations of transcript discovery and quantification in our paper "ESPRESSO: Robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data". All details can be found in the <strong>Materials and Methods</strong> section of the paper. </p> <p><em>HEK293T_DirectRNA.transcriptome_quantification.tsv</em> and <em>HEK293T_DirectRNA.transcriptome_quantification.tsv </em>are tab-separated files containing estimated raw read counts and normalized abundance values (in TPM) of transcripts annotated in GENCODE v34lift37. Transcript quantification was done using NanoSim (version 3.1.0). </p> <p><em>HEK293T_DirectRNA.NanoSim_500k.fastq.gz</em>,<em> </em><em>HEK293T_DirectRNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_DirectRNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_DirectRNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT direct RNA sequencing reads respectively. </p> <p><em>HEK293T_1DcDNA.NanoSim_500k.fastq.gz</em>,<em> HEK293T_1DcDNA.NanoSim_1M.fastq.gz</em>, <em>HEK293T_1DcDNA.NanoSim_3M.fastq.gz</em>, and<em> HEK293T_1DcDNA.NanoSim_5M.fastq.gz </em>are gzip compressed FASTQ files containing 0.5 million, 1 million, 3 million, and 5 million simulated ONT 1D cDNA sequencing reads respectively. </p>
Process-level quantification on opposite PM2.5 changes during COVID-19 lockdown over North China Plain
<p>Observations and WRF-Chem simulation results for manuscript (Process-level quantification on opposite PM<sub>2.5</sub> changes during COVID-19 lockdown over North China Plain) submitted to GRL</p>
Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
<p>Methane detection limits, emission rate quantification accuracy, and potential cross-species interference are assessed for Bridger Photonics' Gas Mapping LiDAR (GML) system utilizing data collected during laboratory testing and single-blind controlled release testing. Laboratory testing identified no significant interference in the path-integrated methane measurement from the gas species tested (ethylene, ethane, propane, n-butane, i-butane, and carbon dioxide). The controlled release study, comprised of 650 individual measurement passes, represents the largest dataset collected to date to characterize GML with respect to point-source emissions. Binomial regression is utilized to create detection curves illustrating the likelihood of detecting an emission of a given size under different wind conditions and for different flight altitudes. Wind-normalized methane detection limits (90% detection rate) of 0.25 (kg/h)/(m/s) and 0.41 (kg/h)/(m/s) are observed at a flight altitude of 500 feet and 675 feet above ground level, respectively. Quantification accuracy is also assessed for emissions ranging from 0.15 to 1400 kg/h. When emission rate estimates were generated using wind from High-Resolution Rapid Refresh (HRRR) model (the primary wind source that Bridger uses for their commercial operations), linear regression indicates bias of 8.1% (R2 = 0.89). For 95% of controlled releases above Bridger's stated production-sector detection sensitivity (3 kg/h with 90% probability of detection), accuracy of individual emission rate estimates produced using HRRR wind ranged from -64.1% to 87.0%. Across all controlled releases 38.1% of estimates had error within +/- 20%, and 87.3% of measurements were within a factor of two (-50% to +100% error). At low wind speed (less than 2 m/s) and low emission rates (less than 3 kg/h) emission estimates are biased high; however, when removed do not impact the regression significantly. The aggregate quantification error including all detected emission events was +8.2% using the HRRR wind source. The resulting detection curves and quantification accuracy illustrate important implications which must be considered when using measurements from GML or other remote emission measurement techniques to inform or validate inventory models, or to audit reported emission levels from oil and gas systems.</p>
Quantification of Error Sources with Inertial Measurement Units in Sports - Data and Matlab Scripts
<p>Inertial measurement units (IMUs) offer the possibility to capture the lower body motions of players of outdoor team sports. However, various sources of error are present when using IMUs: the definition of the body frames, the soft tissue artefact (STA) and the orientation filer. Methods to minimize these errors are currently being used without knowing their exact influence on the various sources of errors. The goal of this study was to quantify each of the sources of error of an IMU separately. An optoelectronic system was used as a golden standard. Rigid marker clusters (RMCs) were designed to construct a rigid connection between the IMU and four markers. This allowed for the separate quantification of each of the sources of error. Ten subjects performed nine different trials, varying both in type of movement and in movement intensity. The error of the definition of the body frames (10.9-18.1 deg RMSD), the STA (3.6-9.4 deg RMSD) and the error of the orientation filter (2.8- 13.1 deg RMSD) were all quantified separately. The data and code to process the data can be found in this publication.</p> <p> </p>
Dataset related to the article "Extraction-Free Absolute Quantification of Circulating miRNAs by Chip-Based Digital PCR"
<p>This record contains raw data related to the article "Extraction-Free Absolute Quantification of Circulating miRNAs by Chip-Based Digital PCR"</p> <p>Circulating microRNAs (miRNA) have been proposed as specific biomarkers for several diseases. Quantitative Real-Time PCR (RT-qPCR) is the gold standard technique currently used to evaluate miRNAs expression from different sources. In the last few years, digital PCR (dPCR) emerged as a complementary and accurate detection method. When dealing with gene expression, the first and most delicate step is nucleic-acid isolation. However, all currently available protocols for RNA extraction suffer from the variable loss of RNA species due to the chemicals and number of steps involved, from sample lysis to nucleic acid elution. Here, we evaluated a new process for the detection of circulating miRNAs, consisting of sample lysis followed by direct evaluation by dPCR in plasma from healthy donors and in the cardiovascular setting. Our results showed that dPCR is able to detect, with high accuracy, low-copy-number as well as highly expressed miRNAs in human plasma samples without the need for RNA extraction. Moreover, we assessed a known myocardial infarction-related miR-133a in acute myocardial infarct patients vs. healthy subjects. In conclusion, our results show the suitability of the extraction-free quantification of circulating miRNAs as disease markers by direct dPCR.</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
Parasite detection and quantification in avian blood is dependent on storage medium and duration
<p>Studies of parasites in wild animal populations often rely on molecular methods to both detect and quantify infections. However, method accuracy is likely to be influenced by the sampling approach taken prior to nucleic acid extraction. Avian Haemosporidia are studied primarily through the screening of host blood, and a range of storage mediums are available for the short- to long-term preservation of samples. Previous research has suggested that storage medium choice may impact the accuracy of PCR-based parasite detection, however, this relationship has never been explicitly tested and may be exacerbated by the duration of sample storage. These considerations could also be especially critical for sensitive molecular methods used to quantify infection (qPCR). To test the effect of storage medium and duration on Plasmodium detection and quantification, we split blood samples collected from wild birds across three medium types (filter paper, Queen's lysis buffer, and 96% ethanol) and carried out DNA extractions at five time-points (1, 6, 12, 24 and 36 months post-sampling). First, we found variation in DNA yield obtained from blood samples dependent on storage medium which had subsequent negative impacts on both detection and estimates of Plasmodium copy number. Second, we found that detection accuracy (incidence of true-positives) was highest for filter paper stored samples (97%), while accuracy for ethanol and Queen's lysis buffer-stored samples was influenced by either storage duration or extraction yield respectively. Lastly, longer storage durations were associated with decreased copy number estimates across all storage mediums; equating a 58% reduction between the first- and third-year post-sampling for lysis-stored samples. These results raise questions regarding the utility of standardising samples by dilution, while also illustrating the critical importance of considering storage approaches in studies of Haemosporidia comparing samples subjected to different storage regimes and/or stored for varying lengths of time.</p>
Data for Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response
<p>Data from simulations used to generate the figures in the paper <em>Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response</em>.</p> <p>To reproduce analyses, extract <em>colony_data.zip</em> in the <em>data</em> folder after cloning the <em>vivarium-ecoli</em> repository.</p> <p>The extracted folder contains the following items:</p> <ul> <li><em>sim_dfs</em>: a folder containing the CSV files that represent a subset of the raw simulation data used for downstream analyses.</li> <li><em>glc_10000_fluxome.csv</em>: Each row represents a reaction in central carbon metabolism (in same order as listed in <em>validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv</em>). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux (mmol/L/hr). Provided as input to <em>ecoli/analysis/centralCarbonMetabolism.py </em>script to reproduce fluxome validation plot.</li> <li><em>glc_10000_proteome_avgs.csv</em>: Each row represents a protein monomer (in same order as <em>sim_data.translation.monomer_data["id"]</em> where <em>sim_data</em> is <em>reconstruction/sim_data/kb/validationData.cPickle</em>). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to <em>ecoli/analysis/proteinCountsValidation.py</em> script to reproduce proteome validation plot.</li> <li><em>glc_10000_expressome.csv</em>: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to <em>ecoli/analysis/antibiotics_colony/subgen_gene_plots/count_subgen.py</em> script to calculate number of sub-generational genes among all genes and antibiotic response genes.</li> <li><em>glc_10000_total_mrna.json</em>: Mapping of agent IDs for all cells in a baseline glucose simulation (seed 10000) to their average total mRNA count. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 2C,D.</li> <li><em>jenner_2013.csv</em>: Data extracted from Fig. 2C of <a href="https://doi.org/10.1073/pnas.1216691110">10.1073/pnas.1216691110</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>olson_2006.csv</em>: Data extracted from Fig. 2D of <a href="https://doi.org/10.1128%2FAAC.01499-05">10.1128/AAC.01499-05</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>lysis_ratios.csv</em>: Data extracted from Fig. 2 of <a href="https://doi.org/10.1099/00221287-31-3-339">10.1099/00221287-31-3-339</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 4N.</li> </ul>
A new method for the quantification of ambient particulate matter emission fluxes - Data
<p>An inversion method has been developed in order to quantify the emission fluxes of certain aerosol pollution sources<br> across a wide region in the Northern hemisphere, mainly in Europe and Western Asia. The data employed are the aerosol<br> contribution factors deducted by Positive Matrix Factorization (PMF) on a PM2.5 chemical composition dataset from 16<br> European and Asian cities for the period 2014 to 2016. The spatial resolution of the method corresponds to the geographic<br> grid cell size of the Lagrangian particle dispersion model 5 (FLEXPART 10.4 , 1 x 1 degree) which was utilized for the air mass backward simulations. The area covered is also related to the location of the 16 cities under study.</p>
YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
<p>This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.</p>
Datasets related to the Eggsplorer rapid plant–insect resistance determination tool, using an automated whitefly egg quantification algorithm
<p>These datasets support the data provided directly in the scientific publication titled "Eggsplorer: a rapid plant–insect resistance determination tool using an automated whitefly egg quantification algorithm", published in the <a href="https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01027-9#Abs1">BMC Plant Methods journal</a>. </p> <p>The figures and tables included in the datasets highlight various materials and methods employed to build a proof-of-concept of the Eggsplorer tool. These included whitefly assays, leaf image acquisitions, an automatic egg quantification algorithm, image pre-processing, object detection and image stitching, detection post-processing, algorithm evaluation and software programming.</p> <p>The provided figures and tables in the datasets are further discussed and interpreted in detail, as well as their subsequent results, in the scientific publication.</p> <p>This research was conducted within the VIRTIGATION project, which is part of the EU Open Research Data pilot. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000570.</p>
Intrinsically Disordered Regions Promote Protein Refoldability and Facilitate Retrieval from Biomolecular Condensates–Peptide Quantifications
<p>Many eukaryotic proteins contain intrinsically disordered regions (IDRs) that intersperse globular folded domains, in contrast with bacterial proteins which are typically highly globular. Recent years have seen great progress in identifying biological functions associated with these elusive protein sequence: in specific cases, they mediate liquid- liquid phase separation, perform molecular recognition, or act as sensors to changes in the environment. Nevertheless, only a small number of IDRs have annotated functions despite their presence in 64% of yeast proteins, stimulating some to question what ‘general purpose’ they may serve. Here, by interrogating the refoldability of two fungal proteomes (Saccharomyces cerevisiae and Neurosporra crassa), we show that IDRs render their host proteins more refoldable from the denatured state, allowing them to cohere more closely to Anfinsen’s thermodynamic hypothesis. The data provide an exceptionally clear picture of which biophysical and topological characteristics enable refoldability. Moreover, we find that almost all yeast proteins that partition into stress granules during heat shock are refoldable, a finding that holds for other condensates such as P-bodies and the nucleolus. Finally, we find that the Hsp104 unfoldase is the principal actor in mediating disassembly of heat stress granules and that the efficiency with which condensed proteins are returned to the soluble phase is also well explained by refoldability. Hence, these studies establish spontaneous refoldability as an adaptive trait that endows proteins with the capacity to reform their native soluble structures following their extraction from condensates. Altogether, our results provide an intuitive model for the function of IDRs in many multidomain proteins and clarifies their relationship to the phenomenon of biomolecular condensation.</p> <p>This dataset provides peptide quantifications (and their respective P-values) from three separate types of experiments used to support the claims in this study.</p> <p>1. Peptide quantifications from global refolding reactions, assessed with limited-proteolysis mass spectrometry (LiP-MS), carried out on two fungal organisms (S. cerevisiae [yeast] & N. crassa), at three refolding times, repeated on three separate iterations (for yeast).</p> <p>2. Peptide quantifications from LiP-MS experiments conducted on yeast extracts during heat shock or recovery from heat shock</p> <p>3. Annotations for peptides in #1 that are associated with linker regions between folded domains.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.