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1,456
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Dataset results
1,456 results for “residues”
MITF-low melanoma subtype models in zebrafish reveal transcriptional sub-clusters and MITF-independent residual disease
GEO Series GSE136900. Danio rerio. 332 samples. Type: Expression profiling by high throughput sequencing.
Single-cell RNAseq analysis (10X Genomics Chromium) of residual bronchoalveolar lavage fluids in healthy, fumer and COPD patients
GEO Series GSE183974. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Biomarkers of residual disease, disseminated tumor cells, and metastases in the MMTV-PyMT breast cancer model
GEO Series GSE43566. Mus musculus. 38 samples. Type: Expression profiling by array.
Profiling of untreated and residual murine BCC after Hedgehog Pathway Inhibitor
GEO Series GSE101504. Mus musculus. 15 samples. Type: Expression profiling by high throughput sequencing.
miR-126 identifies a quiescent and chemo-resistant human B-ALL cell subset that correlates with minimal residual disease [scRNAseq]
GEO Series GSE236136. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
POWERDERESS and HDA9 interact and promote histone H3 deacetylation at specific lysine residues in Arabidopsis.
GEO Series GSE89770. Arabidopsis thaliana. 27 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Dataset for Residual Study: Testing Jupiter Atmosphere Models Against Juno MWR Observations
<p>This dataset includes all data values for all 14 figures in paper "Residual study: Testing Jupiter atmosphere models against Juno MWR observations".</p>
Supplemental Material to Journal Article "Impact of Site-Specific Thermal Residual Stress on the Fatigue of Wind-Turbine Blades"
<p>This set supplements the figure data to the article "Impact of Site-Specific Thermal Residual Stress on the Fatigue of Wind-Turbine Blades", DOI: <a href="https://doi.org/10.2514/1.J059388">10.2514/1.J059388</a>.</p>
Figure 6 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 6 Linearity of IPA solutions.
Figure 5 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 5 Linearity of acetone solutions.
Figure 4 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 4 Chromatogram of specificity (spiked solution of quinabut API, acetone and 2-propanol).
Figure 3 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 3 Typical chromatogram of acetone and IPA standard solution.
Figure 2 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 2 Typical chromatogram of blank (water).
Figure 1 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 1 Chemical structure of quinabut.
Figure 7 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Method development and validation for the determination of residual solvents in quinabut API by using gas chromatography. Message 2. Pharmacia 68(1): 53-59. https://doi.org/10.3897/pharmacia.68.e52119
Figure 7 Chromatogram of quinabut API sample.
Residual Matters: How Residual Learning Can Improve Climate Downscaling
<p><strong>Title</strong>: Residual Matters: How Residual Learning Can Improve Climate Downscaling</p> <p><strong>Description</strong>:<br>This repository contains the code and data used in the project "<em>Residual Matters: How Residual Learning Can Improve Climate Downscaling</em>." This project explores how residual networks, particularly models like EDSR and VDSR, can improve the downscaling of climate data. The goal is to enhance the spatial resolution of climate variables—such as ERA5 2m temperature data—using deep learning approaches. By leveraging residual learning techniques, this project achieves higher accuracy in climate downscaling, particularly in complex terrain regions, providing more reliable data for climate predictions and research.</p> <p>The repository includes:</p> <ul> <li>Python scripts implementing EDSR and VDSR architectures for temperature data downscaling.</li> <li>Requirements file (<code>requirements.txt</code>) listing all dependencies for reproducibility.</li> <li>Sample data preprocessing and model training scripts.</li> <li>Instructions for evaluating model performance.</li> <li><code>LICENSE</code> file under the GNU General Public License (GPL) v3, permitting reuse and modification.</li> </ul> <p><strong>License</strong>: GNU General Public License v3.0</p> <p><strong>Keywords</strong>: Climate Downscaling, Deep Learning, Super-Resolution, Residual Networks, EDSR, VDSR, ERA5</p> <p><strong>Usage Notes</strong>:<br>To run the code, set up a Python environment using the dependencies listed in <code>requirements.txt</code>. The provided data files can be used as input examples, and the README offers detailed instructions on running the models. For further usage guidance, please refer to the README or the paper associated with this project.</p> <p><strong>Acknowledgments</strong>: This work is conducted as part of research at IIT Mandi, focusing on improving climate downscaling techniques with deep learning.</p>
Residual Surge Heights at Cuxhaven (1940-2021), Esbjerg (1950-2019), and Delfzijl (1971-2017)
<p>Hourly time series of residual surge heights at the Cuxhaven (Germany), Esbjerg (Denmark), and Delfzijl (The Netherlands) tide gauges. Residual surge heights were calculated from observed water levels by removing the astronomical tide signal estimated with UTide, and the mean sea-level rise by subtracting the annual mean water level. Periods 1940-2021 for Cuxhaven, 1950-2019 for Esbjerg, and 1971-2017 for Delfzijl.</p>
Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"
<p><strong>Supplements for "The Proteomic Code: Novel Amino Acid Residue Pairing Models “Encode” Protein Folding and Protein-Protein Interactions"</strong></p> <p>This supplement includes the following files:</p> <ul> <li>Main_v02.R --- Script in R language to process protein structures and produce the datasets.</li> <li>datasets_v02.zip --- final filtered version of datasets produced in R language (by "Main_v02.R"). </li> <li>Dataset key v02.txt --- key and descriptions to column names in ("datasets.zip"). </li> </ul> <p>The article featuring these datasets is submitted to:</p> <p>Journal: Computers in Biology and Medicine (Reference: CBM_110033)<br>Title: "The Proteomic Code: Novel Amino Acid Residue Pairing Models "Encode" Protein Folding and Protein-Protein Interactions"<br>Authors: Hameduh, Tareq; Miller, Andrew D. ; Heger, Zbynek; Haddad, Yazan</p> <p> </p> <p> </p>
Database For Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation
Open the record for dataset details and reuse information.
Bacterial Diversity in Earthworm Castings and Vermicompost Derived from Sericulture Residues and Cattle Manure
<p>This study investigated the bacterial diversity in earthworm castings and vermicompost derived from two different substrates: sericulture residues (silkworm frass) and cattle manure. Earthworms were used to process these organic wastes, and 16S rRNA gene sequencing was employed to characterize the bacterial communities in the initial substrates, the earthworm castings, and the final vermicompost products. The results provide insights into how earthworm-mediated decomposition influences the structure and composition of the bacterial microbiome during the vermicomposting process. This information contributes to a better understanding of the microbial ecology dynamics involved in the conversion of agricultural and livestock wastes into value-added, nutrient-rich vermicompost through vermiculture.</p>
ScienceDex guides
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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.