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13,064
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ShareScore release 0.9.0
Dataset results
13,064 results for “predictions”
Raw datasets and code generation for the strength prediction of TBC incorporating CCA and GOS
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Raw strength datasets and code generation for predicting the strengths of TBC modified with SNA and OSP
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Raw datasets and code generation for strength prediction of BCC incorporating CP and CCA
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Gene Signature predicts autoimmune toxicity in metastatic melanoma
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Original features and code of clinical prediction model
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Subclonal mutational load predicts survival and response to immunotherapy in cancers with low to moderate TMB
<p>Here we provide the downloadable links of subclonal reconstruction results by <strong>CliPP</strong>, on both <strong>TCGA</strong> and <strong>PCAWG</strong> datasets. We also provide the source of our in-house simulation data (<strong>CliPPSim4k</strong>), along with a comparison between CliPP and PhyloWGS.</p> <p>For more detailed information about the data, please refer to our paper: <a href="https://www.biorxiv.org/content/10.1101/2024.07.03.601939v1" target="_new" rel="noopener">https://www.biorxiv.org/content/10.1101/2024.07.03.601939v1</a> or visit our web app: <a href="https://bioinformatics.mdanderson.org/apps/CliPP/" target="_new" rel="noopener">https://bioinformatics.mdanderson.org/apps/CliPP/</a></p>
Predictability of linguistic complexity: samples of the corpora
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Deep learning predictions of Atlantic Niños index
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Field and Lab_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Field and Lab observations_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Depressions and Boundary_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 3_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Scripts and data for the manuscript "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon"
<p>This dataset supports the manuscript titled "Transcriptomic profiling of gill biopsies to define predictive markers for seawater survival in farmed Atlantic salmon." It contains comprehensive RNA-seq count data from gill biopsies of approximately 3000 Atlantic salmon smolt, collected during the SynchroSmolt project. The data is supplemented with RNA-seq counts from two prior photoperiod smolt experiments (2013_shortdays and 2017_winterlength) and single-nucleus RNA-seq (snRNA-seq) data from an additional experiment.</p> <p><strong>Key Dataset Elements:</strong><br>- <strong>RNA-seq read counts and metadata</strong> for three experiments, detailing various growth, condition, and survival indicators.<br>- <strong>Scripts for analysis</strong> include differential expression analysis, random forest model preparation and execution, and cell-type-specific gene analysis.<br>- <strong>Intermediate data outputs</strong> such as normalized RNA-seq counts, results from differential expression analyses, and random forest model inputs and outputs.</p> <p><br>This dataset facilitates the exploration of gene expression-based predictive modeling for seawater survival, revealing key insights into the influence of photoperiod history and developmental gene regulation on Atlantic salmon's transition to seawater.</p>
Orthomosaic_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Orthomosaic of the aerial images_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>DSM_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al's meta-analysis
<p>The research that created this data is described in:</p> <ul> <li>Duggan M and Tressoldi P. Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al’s meta-analysis [version 2; referees: 2 approved]. F1000Research 2018, 7:407 (https://doi.org/10.12688/f1000research.14330.2)</li> </ul> <p>Two files are included:</p> <ul> <li>MA_PPAA_Final.xlsx - Database</li> <li>Code.pdf - Code</li> </ul>
Data and code for: A sensory ecology of fear: Eye size predicts moonlight avoidance responses in Neotropical electric fishes
<p>Data in support of: Eye size predicts moonlight avoidance responses in Neotropical electric fishes</p>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
Disentangling Sources of Uncertainty in CLM5 Model Predictions: Water, Energy, and Carbon Fluxes at European Observation Sites
<p>The datasets include:</p> <ul> <li>EC data from Europement measurement sites in <a href="https://www.icos-cp.eu/data-products/2G60-ZHAK">ICOS</a>, <a href="https://fluxnet.org/login/?redirect_to=/data/download-data/">FLUXNETS</a>, and <a href="https://doi.org/10.34731/x9s3-Kr48">COSMOS-Europe</a>.</li> <li>Ensemble simulation data used for analysis</li> </ul> <p>The atmospheric forcings used in driving the model were all local measurements pre-processed using the script in GitHub repository <a href="https://github.com/FedoAIworld/CLM5-Disentangling-Uncertainty/tree/main/00_create_forcing_ds">CLM5-Disentangling-Uncertainty</a>.</p>
An Empirical Study of Next-Line Prediction in Build Systems Using CodeGen
<p>An Empirical Study of Next-Line Prediction in BuildSystems Using CodeGen.</p> <p>Build systems play a crucial role in software development and are responsible for compiling source code into executable programs. Despite their importance, build systems often receive limited attention because their impact is not directly visible to end users. This oversight can lead to inadequate maintenance, frequent build failures, and disruptions that require additional resources. Recognising and addressing the maintenance needs of build systems is essential to preventing costly disruptions and ensuring efficient software production.</p> <p>In this paper, we explore whether applying a Large Language Model (LLM) can reduce the burden of maintaining build systems. We aim to determine whether the prior content in build specifications provides sufficient context for an LLM to generate subsequent lines accurately. We conduct an empirical study on CodeGen, a state-of-the-art Large Language Model (LLM), using a dataset of 13,343 Maven build files. The dataset consists of the Expert dataset from the Apache Software Foundation (ASF) for fine-tuning (9,426 build files) and the Generalised dataset from GitHub for testing (3,917 build files). We observe that (i) fine-tuning on a small portion of data (i.e., 11\% of the fine-tuning dataset) provides the largest improvement in performance by 13.93\% (ii) When applied to the Generalised dataset, our fine-tuned model retains 83.86\% of its performance, indicating that it is not overfitted. Upon further investigation, we classify build code content into functional and metadata subgroups based on enclosing tags. Our fine-tuned model performs substantially better in suggesting functional than metadata build code. Our findings highlight the potential of leveraging LLMs like CodeGen to relieve the maintenance challenges associated with build systems, particularly in functional content. The study highlights the limitations of large language models in suggesting the metadata components of build code. Future research should focus on developing approaches to enhance the accuracy and effectiveness of metadata generation.</p> <p> </p> <p>Replication Package Structure:</p> <p>dataset.zip: Contains dataset for Expert and Generalised dataset.</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.