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Dataset results
14 results for “multi-modal model”
A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys
<p>A multi-type geobody dataset for training SAG model, including channel, paloekarst, salt body, and so on.</p> <p>A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (<a href="https://arxiv.org/abs/2409.04962">[2409.04962] A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys (arxiv.org)</a>)</p> <p> </p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
<p>Assigning accurate property labels to proteins, like functional terms and catalytic activity, is challenging, especially for proteins without homologs and "tail labels" with few known examples. Unlike previous methods that mainly focused on protein sequence features, we use a pretrained large natural language model to understand the semantic meaning of protein labels. Specifically, we introduce FAPM, a contrastive multi-modal model that links natural language with protein sequence language. This model combines a pretrained protein sequence model with a pretrained large language model to generate labels, such as Gene Ontology (GO) functional terms and catalytic activity predictions, in natural language. Our results show that FAPM excels in understanding protein properties, outperforming models based solely on protein sequences or structures. It achieves state-of-the-art performance on public benchmarks and in-house experimentally annotated phage proteins, which often have few known homologs. Additionally, FAPM's flexibility allows it to incorporate extra text prompts, like taxonomy information, enhancing both its predictive performance and explainability. This novel approach offers a promising alternative to current methods that rely on multiple sequence alignment for protein annotation.</p>
FAPM: Functional annotation of proteins using multi-modal models beyond structural modeling
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plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
Data and code availibility for the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al.
<p>The data and codes used in the paper "Ice-nucleating agents in sea spray aerosol identified and quantified with a holistic multi-modal freezing model" by Alpert et al., published in <em>Science Advances</em> are included in this collection. Detailed descriptions of the files are given in the readme file.</p>
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
<p>The SIM2 testing set.</p>
Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
<p>The SIM1 testing set.</p>
Multi-modal single cell analysis reveals gut microbiota reshapes brain immune landscape in aged mouse model.
GEO Series GSE160628. Mus musculus. 13 samples. Type: Other.
Study on Multi-Modality Models of Cardiac Function in Healthy Individuals
ClinicalTrials.gov study NCT06952673. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Deep Learning Model for Blood Volume Estimation From Multi-modal Ultrasound
ClinicalTrials.gov study NCT06957587. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Risk Prediction Model of MACE in Patients With AMI Based on Multi-modal Machine Learning
ClinicalTrials.gov study NCT06767852. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multi-modal single cell analysis reveals gut microbiota reshapes brain immune landscape in aged mouse model
GEO Series GSE148127. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling
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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.