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COVID-19 vaccine uptake data set
<p>The novel Coronavirus Disease 19 (COVID-19) caused devastating effects globally, and healthcare workers were among the most affected by the pandemic. Despite healthcare workers being prioritized in COVID-19 vaccination globally and in Ghana, hesitancy to receive the vaccines resulted in delayed control of the pandemic. In Ghana, healthcare workers had a vaccine acceptance of 39.3% before the vaccine rollout. Consequently, this study assessed the uptake of COVID-19 vaccination and associated factors among healthcare workers in Ghana in the post-vaccine roll-out period. This was an analytical cross-sectional study that used a semi-structured questionnaire to collect data on COVID-19 vaccination uptake and influencing factors. 256 healthcare workers were selected in Ayawaso West Municipality of Ghana using a stratified random sampling approach. Descriptive statistics were used to examine socio-demographic factors and Likert scale responses. Bivariable and Multivariable logistic regression were performed using IBM SPSS version 22 to identify predictors of vaccine uptake and a statistical significance was declared at p<0.05. More than three-fourths of participants 220 (85.9%) had received at least one dose of the COVID-19 vaccination, while 36 (14.9%) were hesitant. More than half 139 (54.3%) had adequate knowledge about COVID-19 vaccination and the majority 188 (73.4%) had positive perceptions about its effectiveness. Moreover, 218 (85.2%) of HCWs had a positive attitude towards COVID-19 vaccination. Positive attitude towards COVID-19 vaccination (AOR = 4.3; 95% CI: 1.4, 13.0) and high cues to action (AOR = 5.7; 95% CI: 2.2, 14.8) were the factors that significantly predicted uptake of COVID-19 vaccination among healthcare workers. COVID-19 vaccination among HCWs in Ghana is promising. However, hesitancy to receive the vaccination among a significant proportion of HCWs raises concerns. To ensure vaccination of all healthcare workers, interventions to promote vaccination should target key determinants of vaccination uptake, such as attitude towards the vaccination and cues to action.</p>
Using a standardized sound set to help characterize misophonia: The international affective digitized sounds
<p>Misophonia is a condition characterized by negative affect, intolerance, and functional impairment in response to particular repetitive sounds usually made by others (e.g., chewing, sniffing, pen tapping) and associated stimuli. To date, researchers have largely studied misophonia using self-report measures. As the field is quickly expanding, assessment approaches need to advance to include more objective measures capable of differentiating those with and without misophonia. Although several studies have used sounds as experimental stimuli, few have used standardized stimuli sets with demonstrated reliability or validity. To conduct rigorous research to better understand misophonia, it is important to have an easily accessible, standardized set of acoustic stimuli for use across studies. Accordingly, in the present study, the International Affective Digitized Sounds (IADS-2), developed by Bradley and Lang [1], were used to determine whether participants with misophonia responded to certain standardized sounds differently than a control group. Participants were 377 adults (132 participants with misophonia and 245 controls) recruited from an online platform to complete several questionnaires and respond to four probes (arousal, valence, similarity to personally relevant aversive sounds, and sound avoidance) in response to normed pleasant, unpleasant, and neutral IADS-2 sounds. Findings indicated that compared to controls, participants with high misophonia symptoms rated pleasant and neutral sounds as significantly more (a) arousing and similar to trigger sounds in their everyday life, (b) unpleasant, and (c) likely to be avoided in everyday life. For future scientific and clinical innovation, we include a ranked list of IADS-2 stimuli differentiating responses in those with and without misophonia, which we call the IADS-M.</p>
Clermont validation set
<p>This is the validation set presented in [1]. </p> <p>It was used to test the validitity of EzClermont. It includes 125 <em><em><a href="http://doi.org/10.1601/nm.3093" target="_blank" rel="noopener">E. coli</a> </em></em>genomes where the isolates have had Clermont phylotypes experimentally determined. </p> <div> <div>It is presented here as a benchmark set for other tools implementing in silico prediction of Clermont phylotypes. </div> <div> </div> <div>[1]: Waters NR, Abram F, Brennan F, Holmes A, Pritchard L. Easy phylotyping of <em>Escherichia coli via</em> the EzClermont web app and command-line tool. Access Microbiol. 2020 Jun 19;2(9):acmi000143. doi: 10.1099/acmi.0.000143. PMID: 33195978; PMCID: PMC7656184.</div> </div>
California MPA network ROV data set and code
<p>Dataset of remotely operated vehicle (ROV) surveys conducted across California's MPA network between 2005 and 2021 and code to conduct analyses and produce plots in the manuscript "Diving deep into the network: quantifying protection effects across California's marine protected area network using a remotely operated vehicle".</p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Data set for "Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites"
<p>EBSD raw data used in the publication:</p> <p>Zertani, S., Morales, L.F.G., Menegon, L. (2024). Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites. Contributions to Mineralogy and Petrology, 179:xx. https://doi.org/10.1007/s00410-024-02125-0</p>
Exhaustive Symbolic Regression Function Sets
<p>ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions. This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.</p> <p>Here we provide the functions generated, the unique equations, and the mappings between all equations and unique ones using different sets of basis functions. These are:</p> <ul> <li>"core_maths": <span>\(\{x, a, {\rm inv}, +, -, \times, \div, {\rm pow} \}\)</span></li> <li>"ext_maths": <span>\(\{x, a, {\rm inv}, \sqrt{\cdot}, {\rm square}, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> <li><span>"base_e_maths": \(\{x, a, {\rm inv}, \exp, \log, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> </ul> <p>where <span>\(x\)</span> is the input variable and <span>\(a\)</span> denotes a constant.</p> <p>One can fit these functions to a data set of interest by using the <a href="https://esr.readthedocs.io">ESR package</a>.</p>
Data sets to the paper "Weakening surface hydrogen to enhance permeation in hydrogen selective membranes", E. Billeter and A. Borgschulte, Appl. Surf. Sci. (2024)
<p>Datasets to empirical data displayed in Figs. 1(c), 2(b), 3(a), 4(b), 4(c)</p>
BraggSpotFinder Data Set (BSD)
<p>This is the data set that was used to train and test a new BraggSpotFinder application based on the U-Net convolutional neural network.</p> <p>Data set consists on 304 individual CBF diffraction frames from 49 crystals. </p> <p>All diffraction frames were acquired using the rastering method at the AMX beamline, NSLS-II. </p> <p>See description in article: Dong et al., J. Appl. Cryst. (2024) https://doi.org/10.1107/S1600576724002450</p> <p> </p> <p> </p>
IG receptor germline set for species: Mouse subgroup: 129S1/SvImJ set_name: 129S1/SvlmJ IGLV
<p>IG Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
IG receptor germline set for species: Mouse subgroup: BALB/c/ByJ set_name: BALB/c/ByJ IGLV
<p>Germline Reference set published on the Open Germline Receptor Database (OGRDB)</p>
SHOWCASE Task 2.7 Biodiversity Indicators and Predictors data set
<p>The dataset contains five biodiversity (0-1 interval normalised) indicators from five case study areas (HU, ES, PT, NL and CH): wild bees abundance (field "Ind_WBA"), wild bees species richness (field "Ind_WBR"), spiders abundance (field "Ind_SpA"), spider species richness (field "Ind_SpR"), and vascular plants species richnes (field "Ind_PlaR"). Data are georeffered (EPSG: 3035) and coordinates are provided (field:" xcoord_EPSG3035" and "ycoord_EPSG3035"). Data were collected in two sampling rounds (field "Round"). Additionally the data set contains a set<span> of predictors for biodiversity indicators encompassing 27 variables, belonging to four different groups: 1. </span><span><span><span> </span></span></span><span>Landscape elements: proximity to roads and proximity to Small Woody Features (SWF, Copernicus land Monitoring Services, CLMS 2018); 2.<span> </span></span><span>Terrain descriptors: elevation, aspect, slope, and their derivatives (8 variables); 3. </span><span>Spectral signatures and Remote Sensing Indicatora (RSI) from Copernicus Sentinel 2 (14 variables); and </span><span><span>4.<span> </span></span></span><span>Biodiversity management (3 variables, dummy coded 0,1). </span></p> <table> <tbody> <tr> <td> <p><strong><span>Group </span></strong></p> </td> <td> <p><strong><span>Predictor </span></strong></p> </td> <td> <p><strong><span>Unit</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> <p><strong><span>Source</span></strong></p> </td> <td> <p><strong><span>Resolution</span></strong></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Road proximity<sup>a</sup></span></p> </td> <td> <p><span>m </span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>SWF proximity<sup>b</sup></span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Elevation</span></p> </td> <td> <p><span>m a.s.l.</span></p> </td> <td> <p><span>Copernicus DEM</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Aspect</span></p> </td> <td> <p><span>degree from North</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Mod. Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Topographic. wetness Index </span></p> </td> <td> <p><span>m/rad</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Valley depth</span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>BI, bare Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Blue (B2, 490 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Green (B3, 560 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>IR, infra-red (B8, 842 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDBSI, Norm. Diff. Bare Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDSI, Normalized Diff. Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDVI, Norm. Diff. Vegetation Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NIR, Near Infra-Red (B8A, 865 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Red (B4, 665 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSa, Soil Salinity </span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI1, Soil Salinity Index1</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI2, Soil Salinity Index2</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI3, Soil Salinity Index3</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SWIR Short Wave IR (B11,1610 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Biodiversity Intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Year of intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Round </span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> </tbody> </table> <p><sup><span>a</span></sup><span> Source of vector data: Open Street Map. © OpenStreetMap contributors. Available under the Open Database License from: openstreetmap.org. </span></p> <p><sup><span>b</span></sup><span> Source of raster data (res. 5 m): </span><span><a href="https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018"><span>https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018</span></a></span><span>. </span><span><a href="https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018"><span>https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018</span></a></span><span> </span></p>
finnpiatscheck/Environmental-Effects-on-a-Fig-Wasp-Community: Data sets and R scripts
<p>These are the data and R scripts used in the paper "Landscape-Level Analysis of a Fig-Pollinator-Antagonist Community: Spatial and Temporal Variation in a Fig Wasp Community and its Response to Biotic and Abiotic Factors".</p>
Mugger data set of Bardia NP
Open the record for dataset details and reuse information.
Example data sets and input parameters for running various features in the Python code Dynpy
<p>This is a set of examples intended to be used with the Python code called Dynpy at <a href="https://zenodo.org/records/13241475">https://zenodo.org/records/13241475</a></p>
SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis
<h1>SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis</h1> <div> </div> <div>SaintGSE is an artificial intelligence model designed to predict human gene-pathway relationships using large-scale differentially expressed gene (DEG) datasets. By leveraging an autoencoder and the SAINT transformer model, SaintGSE overcomes challenges in gene expression analysis, such as data scarcity, model compatibility, and interpretability. This project fine-tuned codes from the SAINT project (https://github.com/somepago/saint), licensed under the Apache License 2.0. </div> <div> </div> <div> </div> <h2>Key Features</h2> <div> </div> <div> * AI-Driven Pathway Prediction: Uses autoencoders and the SAINT model to analyze gene expression data and predict related signaling pathways.</div> <div> </div> <div> * Osteoarthritis Study: Applied to osteoarthritis (OA) to identify key pathways and potential therapeutic targets.</div> <div> </div> <div> * Explainability: Utilizes Shapley additive explanations (SHAP) to interpret model predictions and identify influential genes.</div> <div> </div> <div> </div> <h2>Installation</h2> <div> </div> <div>Before installation, we recommend to build a conda environment from the attached yml file and activate it.</div> <div>Our code has been tested with python=3.8 on linux.</div> <div> </div> <div>```</div> <div>$ cd /path/to/SaintGSE</div> <div>$ conda env create -f saintgse_env.yml</div> <div>$ conda activate saintgse_env</div> <div>```</div> <div> </div> <div>After downloading all the files, please extract the contents of all compressed directories by running the following command in your terminal:</div> <div> </div> <div>```</div> <div>$ find . -name "*.tar.gz" -exec tar -xzvf {} \;</div> <div>$ rm *.tar.gz</div> <div>```</div> <div> </div> <div>Once the file structure is formed as follows, the preparation for using SaintGSE is complete.</div> <div> </div> <div>```</div> <div>.</div> <div>├── datasets</div> <div>│ ├── AE_100cycle_model.pth</div> <div>│ ├── AE_enrichment.tsv</div> <div>│ ├── AEshap</div> <div>│ │ ├── shap_values_latent_dim_1.csv</div> <div>│ │ ├── shap_values_latent_dim_2.csv</div> <div>│ │ ├── shap_values_latent_dim_3.csv</div> <div>│ │ ├── ...</div> <div>│ │ ├── ...</div> <div>│ │ └── shap_values_latent_dim_256.csv</div> <div>│ ├── bestmodels</div> <div>│ │ └── binary</div> <div>│ │ ├── Chronic_Myeloid_Leukemia</div> <div>│ │ │ └── testrun</div> <div>│ │ │ └── saint_gse_model.pth</div> <div>│ │ │── ...</div> <div>│ │ └── Selencompound_Biosynthesis</div> <div>│ │ └── testrun</div> <div>│ │ └── saint_gse_model.pth</div> <div>│ ├── gene_list.pkl</div> <div>│ ├── MGI_Gene_Model_Coord.tsv</div> <div>│ └── pathway_list_in_DEG.txt</div> <div>```</div> <div> </div> <div> <p>The code in this dataset is also accessible via GitHub. You can find the GitHub repository at the following link:</p> <p><a href="https://github.com/MSjeon27/SaintGSE" target="_new" rel="noopener">https://github.com/MSjeon27/SaintGSE</a></p> </div> <div> </div> <h2>DEG dataset preparation</h2> <div>Prior to SaintGSE analysis, prepare DEG data to be used as input in .tsv format as follows. In the column, the official gene symbol of DEGs is located, and the row adds the log2 fold change value in each DEG group. An example is as follows.</div> <div> </div> <div>```</div> <div>LAP3 CD99 HS3ST1 MAD1L1 LASP1 SNX11</div> <div>'mock-6' vs 'LPS-6' -1.3 0 2.4 0 0.7 0</div> <div>'mock-6' vs 'EBOV-6' -1.3 0 2.3 0 0.6 0</div> <div>```</div> <div> </div> <h2>Usage</h2> <div> </div> <h3>Step 0. Preprocessing the input DEG (from pyDESeq2 result)</h3> <div> </div> <div>Currently, SaintGSE has the function of converting mouse genes into human genes. The preprocessing code serves to change the human or mouse DEG data into the format used for SaintGSE.</div> <div> </div> <div>* human DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div>* mouse DEGs</div> <div>```</div> <div>$ preprocessing.py --query_fc /path/to/your/DEGs.tsv --org mouse --out Preprocessed_fc.tsv</div> <div>```</div> <div> </div> <div> </div> <h3>Step 1. Training SaintGSE for a target pathway</h3> <div> </div> <div>SaintGSE can be used to analyze new gene expression datasets for pathway prediction:</div> <div> </div> <div>```</div> <div>$ SaintGSE.py --pathway 'Proteins Involved in Osteoarthritis' --pretrain</div> <div>```</div> <div> </div> <div> </div> <h3>Step 2. Prediction through SaintGSE</h3> <div> </div> <div>```</div> <div>$ SaintGSE.py --predict Preprocessed_fc.tsv --pathway 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The results of the predictions are as follows.</div> <div> </div> <div>```</div> <div>tensor([[1.]], device='cuda:0')</div> <div>```</div> <div> </div> <div>This indicates that your DEG data is related to the target signaling path.</div> <div> </div> <div> </div> <div> </div> <h3>Step 3. Interpretation the result of SaintGSE (Get Relative SHAP contribution for each DEGs)</h3> <div>```</div> <div>$ Interpret.py -d Preprocessed_fc.tsv -p 'Proteins Involved in Osteoarthritis'</div> <div>```</div> <div> </div> <div>The result of interpretation produces the following files for each sample.</div> <div> </div> <div>```</div> <div><Sample_name>_<target_pathway>_significant_gene_shap.csv</div> <div>```</div> <div> </div> <div>This represents the relative SHAP contribution for each gene in the DEG data. In the subsequent analysis, it is recommended to focus on the genes with the relative SHAP contributions in the top 35% to 50% as we suggested in the paper, depending on the number of DEGs.</div> <div> </div> <div> </div> <h2>How to Cite</h2> <div> </div> <div>If you use this model or repository in your research, please cite it as follows:</div> <div> </div> <div>```</div> <div>Jeon, MS & Nam, JH et al., "SaintGSE: Transformer-based efficient and explainable gene set enrichment analysis," 2024. GitHub repository. Available at: https://github.com/MSjeon27/SaintGSE</div> <div>```</div> <div> </div> <div>For more information or any questions regarding citation, feel free to contact us (msjeon27@cau.ac.kr).</div> <p> </p>
Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
CryoXKit virtual screening set
<p>Virtual screening dataset used in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6723b0a0f9980725cfb49751"><em>Docking guidance </em><em>with experimental ligand structural density </em><em>improves docking pose prediction and virtual </em><em>screening performance</em></a>. Modification of <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155">LIT-PCBA</a> dataset. The dataset used for TRPV1 is available from <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00312">Llanos et al.</a></p> <p> </p> <p>Also included are </p> <pre><code>run_bias.template.qsub</code></pre> <pre><code>submit.sh</code></pre> <p>Together, these scripts provide examples of how to set-up and run screening density-guided simulations on a SLURM-based HPC cluster. Please note that this dataset does not include the structural density files associated with each receptor. These must be downloaded separately from the RCSB PDB (hyperlinks for each receptor may be found in SI Table 2 of the associated publication for this dataset).</p> <p> </p> <p><strong>Targets included:</strong></p> <p>ADRB2</p> <p>ALDH1</p> <p>FEN1</p> <p>GBA</p> <p>HSP90a</p> <p>MAPK1</p> <p>MTORC1</p> <p>PKM2</p> <p>VDR</p>
Data set of axle loads and distances for operating passenger and freight trains for development of load models
<p>Train data for moving load models of operating trains (5,007 passenger trains and 139,182 freight trains).</p> <ul> <li>axle distances [m] and axle loads [kN] for all trains included in zip-files as txt-files</li> <li>information on line categories (DIN EN 15528), loadcases and maximum speeds for passenger trains (and assignment to passenger train numbers in previous version of publication (used for development of load model)) in xlsx-file</li> </ul>
Application Case 3: Data sets consisting of 3D scans, 3D model and the derived metadata, from two different software programs
<p>In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.</p> <p>The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.</p> <p>The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. <em>et al.</em> Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. <em>Herit Sci</em> <strong>9</strong>, 91 (2021). <a href="https://doi.org/10.1186/s40494-021-00561-w">https://doi.org/10.1186/s40494-021-00561-w</a></p> <p>Python scripts for exporting metadata can be found here: <a href="https://github.com/i3mainz/3dcap-md-gen/tree/0.1.3">GitHub - i3mainz/3dcap-md-gen</a></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.