Find research datasets worth reusing

opencc-by-4.0May 2024View details →

Quantification of membrane fluidity in bacteriaTIR-FCS: Research data

Dataset supporting the research paper: Barbotin A, Billaudeau C, Sezgin E, Carballido-López R, Quantification of membrane fluidity in bacteria using TIR-FCS, Biophysical Journal (2024), doi:<a href="https://doi.org/10.1016/"> https://doi.org/10.1016/j.bpj.2024.06.012</a>

opencc-by-4.0Jan 2024View details →

Data for "Uncertainty quantification in geochemical mapping: a review and recommendations"

Data for "Uncertainty quantification in geochemical mapping: a review and recommendations".

opencc-by-4.0Jul 2024View details →

Towards Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance

This repository contains the implementation of Robust Hemolysis Modeling with Uncertainty Quantification: A Universal Approach to Address Experimental Variance. The provided Python script demonstrates the construction of the MCMC (Markov Chain Monte Carlo) method and illustrates how to use MCMC for generating hemolysis distributions. Please note that the actual hemolysis calculations should be performed using your preferred CFD (Computational Fluid Dynamics) software. If there are any questions, please contact: blum@ame.rwth-aachen.de

opencc-by-sa-4.0Aug 2018View details →

Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"

Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15). All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz. Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.

opencc-by-4.0Nov 2018View details →

Expression quantification from the killifish, Fundulus rathbuni (gill epithelium)

Files created with the salmon quantification tool using the reference de novo transcriptome assembly from the killifish, Fundulus rathbuni. Fish were acclimated to either brackish or fresh water then exposed to an acute brackish water challenge. Transcriptome data from gill epithelium tissue were collected. A reference transcriptome assembly was generated from all individuals then used to analyze transcriptional responses to salinity.

opencc-by-4.0Feb 2019View details →

Simulated quantification files for "Swimming downstream" workflow (bias 1-6 + uniform 7-12)

Simulated quantification files for "Swimming downstream" workflow <a href="https://doi.org/10.12688/f1000research.15398.3">https://doi.org/10.12688/f1000research.15398.3</a> Salmon (0.11.3) with Gibbs samples and kallisto (0.44.0) quantification files for the samples in two condition groups and two balanced batches: with realistic bias (1-6) and samples with uniform coverage (7-12). 24 samples in total. Reference: Gencode v28 human transcripts.

opencc-by-4.0Aug 2018View details →

Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 3

Images used for quantification of PIF4-HA and PIF5-HA protein stability in Fig 3. Hayes et. al 2018. 35S:PIF4-HA and 35S:PIF5-HA (Ler) were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).   Experiment was performed on 4 dates (in duplicate). R= red light FR= red +far-red light RN= red light +NaCl FRN= red +far-red light +NaCl   Sample order is as follows:   01-08-18: PIF4-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2 PIF5-HA: R1, R2, FR1, FR2, RN1, RN2, FRN1, FRN2   08-08-18: PIF4-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4 PIF5-HA: R3, R4, FR3, FR4, RN3, RN4, FRN3, FRN4   15-08-18: PIF4-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6 PIF5-HA: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6   10-10-18: PIF4-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8 PIF5-HA: R7, FR7, RN7, FRN7, R8, FR8, RN8, FRN8

opencc-by-4.0Dec 2018View details →

Image used for GUS staining quantification in Hayes et al (2019) Current Biology: Figure 3

DR5v2:GUS plants in the Col or pif4-101/pif5 background were germinated for 3 days on soil (white light- 16:8h photoperiod), before being transferred to soil with or without 25mM NaCl. Plants were then grown in Wl for a further day. On day 4, at Zt 4.5, half the plants were moved to white light + far-red light. Tissues were fixed at Zt 4.5 on day 6 by 20 minutes in 90% acetone at -20°C. Tissues were washed twice under a vacuum in GUS wash solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 2 mM K3Fe(CN)6) and then stained overnight at 37°C in GUS staining solution (0.1M Phospho-PI pH 7.0, 10 mM EDTA, 1 mM K3Fe(CN)6, 1mM K4Fe(CN)6 * 3H2O, 0.5 mg/ ml X-Gluc). The GUS reaction was inhibited by treatment with 3:1 ethanol: acetic acid mix for 1 hour at 37°C. Tissues were cleared with 70% ethanol over several days and then mounted in 10% chloral hydrate, 30% glycerol. Slides were scanned and the images used for quantification.   Sample order is as follows:   pif4pif5 Wl                                     Col Wl pif4pif5 Wl                                     Col Wl pif4pif5 Wl+ NaCl                          Col Wl+ NaCl pif4pif5 Wl+ NaCl                          Col Wl+ NaCl pif4pif5 Wl +FR                              Col Wl +FR          pif4pif5 Wl +FR                              Col Wl +FR          pif4pif5 Wl +NaCl +FR                  Col Wl +NaCl +FR pif4pif5 Wl +NaCl +FR                  Col Wl +NaCl +FR

opencc-by-4.0Nov 2018View details →

Images used for protein quantification in Hayes et al (2019) Current Biology: Figure 4

Images used for quantification of BES1-GFP protein stability in Fig 4. Hayes et. al 2019. 35S:BES1-GFP plants were germinated on ½ MS plates in white light (16:8h photoperiod) for 3 days, before being transferred to plates with or without 75mM NaCl. Plates were then grown in red light or a further 2 days. On day 5, at Zt 3, half the plates were moved to red+ far-red light. Tissues were harvested at Zt 4. 10 seedlings were homogenized and proteins were extracted in 70 µl Cracking Buffer (125mM Tris pH 7.4, 2% SDS, 10% Glycerol, 6M Urea, 5% βME) and 15 µl of the extract was run on a 10% polyacrylamide gel. Blots were probed with anti-GFP-HRP (1:1000). Membranes were developed with 50/50 ‘pico’ and ‘femto’ chemiluminescence substrate (Thermo) on a ChemiDoc (Biorad).   Experiment was performed on 3 dates (in duplicate). R= red light FR= red +far-red light RN= red light +NaCl FRN= red +far-red light +NaCl   Sample order is as follows:   01-08-18: BES1-GFP: R1, FR1, RN1, FRN1, R2, FR2, RN2, FRN2   08-08-18: BES1-GFP: R3, FR3, RN3, FRN3, R4, FR4, RN4, FRN4   15-08-18: BES1-GFP: R5, FR5, RN5, FRN5, R6, FR6, RN6, FRN6

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Excel files include hyperspectral indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments. Scripts used for producing plots in the publication and supplementary material are available on Renku; see the Software section. Hyperspectral data are submitted separately due to their size; see the Related works.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a

Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification

This project combines the emerging field of Domain Adaptation with Uncertainty Quantification, working towards applying machine learning to real scientific datasets with limited labelled data. For this project, simulated images of strong gravitational lenses are used as source and target dataset, and the Einstein radius θ E and its uncertainty are determined through regression. Applying machine learning in science domains such as astronomy is difficult. With models trained on simulated data being applied to real data, models frequently underperform - simulations cannot perfectlty capture the true complexity of real data. Enter domain adaptation (DA). The DA techniques used in this work use Maximum Mean Discrepancy (MMD) Loss to train a network to being embeddings of labelled "source" data gravitational lenses in line with unlabeled "target" gravitational lenses. With source and target datasets made similar, training on source datasets can be used with greater fidelity on target datasets. Scientific analysis requires an estimate of uncertainty on measurements. We adopt an approach known as mean-variance estimation, which seeks to estimate the variance and control regression by minimizing the beta negative log-likelihood loss. To our knowledge, this is the first time that domain adaptation and uncertainty quantification are being combined, especially for regression on an astrophysical dataset.

Data associated with the study: Unlocking DAS amplitude information through coherency coupling quantification

Data associated with the study: Unlocking DAS amplitude information through coherency coupling quantification Here we include all DAS data used in the study that is not included in an open access repository elsewhere. Contents of this repository are: Rutford icestream data: 1. Rutford_ice_stream_das_data/icequakes_information.csv - A csv file containing icequake information, including origin times and seismic moment. 2. Rutford_ice_stream_das_data/tdms/*.tdms - Raw DAS data recordings over the time periods when the icequakes occured. Data is recorded by a Silixa iDas. (All other information on the deployment can be found in Hudson et al. (2021), JGR). Gornergletscher data: 3. Gornergletscher_das_data/gornerglethscer_das_qm_stations.csv - A file containing coordinates of all the fibre channels. 4. Gornergletscher_das_data/segy/*.sgy - Raw data files for the time periods used in this study.

Gold, Lanthanum, Gallium, Cobalt and Tantalum quantification performed with relative standardization-NAA in candidate electronic waste (LED) reference material within the METROCYCLEEU project

Datasets containing uncertainty budgets of measurements performed with Neutron Activation Analysis (NAA) on electronic waste materials (specifically light-emitting diodes, LED). Results reported in the datasets are part of the characterization of CRM candidate material LED. Data are elaborated and resulting dataset files are obtained with INAA-INRIM 3.1 software, pre-release development version.

Image dataset for disease quantification in barley and wheat using the Macrobot system and BluVision Macro software

This dataset contains multi-spectral high-resolution macroscopic images acquired using the Macrobot system, tailored for the automated analysis of plant disease phenotyping. The dataset includes images focused on three specific diseases:  <ul> <li>Bipolaris sorokiniana on barley, with images captured 8 days after inoculation </li> <li>Blumeria graminis f. sp. hordei resp. tritici (wheat powdery mildew) on wheat with images captured 8 days after inoculation</li> <li>Puccinia striiformis (yellow rust) on wheat, with images taken 15 days after inoculation</li> </ul> For each sample, the dataset includes individual R, G, B, UV, and backlight illumination images, along with a combined RGB preview image. The BluVision Macro software is compatible with this dataset, enabling precise quantification and analysis of disease severity on barley and wheat leaves.

Dataset for "Detection and quantification of ergothioneine in human serum using surface enhanced Raman scattering (SERS)"

Open the record for dataset details and reuse information.

Yields, Cannabinoids Quantification, and Predictive Programming Codes Using Machine Learning for Non-Psychoactive Cannabis Flowers and Extracts (Cannabis sativa L.) Cultivated in Ecuador.

This publication presents data from various extraction methods, including maceration, Soxhlet, and supercritical fluids, performed on different cannabis flower varieties (Cannabis sativa L.) under varying operating conditions. We quantified the amounts of CBD, THC, CBG, and CBN in the extracts produced by each method using High-Performance Liquid Chromatography (HPLC). Using this data, we developed a machine learning algorithm in RStudio to make predictions and determine the best conditions and yields for each extraction method. The analysis focuses on different varieties of non-psychoactive cannabis cultivated in Ecuador at altitudes over 2,450 m.a.s.l.