{"data":{"id":"10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d","type":"dois","attributes":{"doi":"10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d","prefix":"10.4121","suffix":"df559dc9-6c7f-4766-9c10-dfdb065ab06d","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Miyase Tekpinar","nameType":"Personal","givenName":"Miyase","familyName":"Tekpinar","nameIdentifiers":[{"nameIdentifier":"0000-0003-0937-225X","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Jelle Komen","givenName":"Jelle","familyName":"Komen","affiliation":[],"nameIdentifiers":[]},{"name":"Hana Valenta","givenName":"Hana","familyName":"Valenta","affiliation":[],"nameIdentifiers":[]},{"name":"Ran Huo","nameType":"Personal","givenName":"Ran","familyName":"Huo","nameIdentifiers":[{"nameIdentifier":"0009-0003-2843-1355","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Klarinda de Zwaan","nameType":"Personal","givenName":"Klarinda","familyName":"de Zwaan","nameIdentifiers":[{"nameIdentifier":"0009-0002-7410-8375","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Peter Dedecker","givenName":"Peter","familyName":"Dedecker","affiliation":[],"nameIdentifiers":[]},{"name":"Nergis Tömen","nameType":"Personal","givenName":"Nergis","familyName":"Tömen","nameIdentifiers":[{"nameIdentifier":"0000-0003-3916-1859","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Kristin Grußmayer","givenName":"Kristin","familyName":"Grußmayer","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Raw data underlying the publication: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Other Biological Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0699"},{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"Optical Physics","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0205"},{"subject":"Other Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1099"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Physical Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"02"},{"subject":"Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"10"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"Biological Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"06"},{"subject":"fluctuation microscopy simulations"},{"subject":"foundation AI models"},{"subject":"convolutional gated recurrent unit (convGRU)"},{"subject":"fluorescence proteins, DNA-PAINT"},{"subject":"real-time imaging, live-cell, high-throughput"}],"contributors":[{"name":"TU Delft, Department of Bionanoscience and Kavli Institute of Nanoscience, Grussmayer Lab","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision Lab","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]},{"name":"KU Leuven, Department of Chemistry, Dedecker Lab","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-07-17","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1101/2025.06.05.658028","relatedIdentifierType":"DOI"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[".zip, .txt, .tiff, . pt, .pth, .ts"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution Non Commercial Share Alike 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-sa-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"RESURF DATA REPOSITORY:\u003cbr\u003eThis repository provides data which is used for our paper: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging (RESURF). RESURF is a deep-learning based real-time SR imaging method. We used a recurrent neural network based model ( MISRGRU) and trained it using realistic fluorescence microscopy simulations. We simulated a large dataset containing imaging conditions of different SNRs and blinking conditions and used it to create multiple foundation models. We fine-tuned foundation models with smaller datasets ( either experimental or simulated). In this repository, we provide the complete dataset we used for training and testing as well as foundation models and fine-tuned models.\u003cbr\u003e1- Foundation Models, created by using large synthetic dataset (FWHM:2.2 pixels)2- Live-cell Models, used in Figure 3-4-5-6 (FWHM:2.7 pixels)3- Simulations Foundation_models_testdata: Synthetic test set used for testing the foundation models (Figure 1-2) Live_cell_simulations: Simulations with high-background imitating fluorescence protein conditions which is used for training live-cell models (transfer learning from foundation models)4- Live-cell experiments used in Figure 3-45- DNA-PAINT models, used in Figure 7 (FWHM:3.8 pixels)6- DNA PAINT experiments, used in Figure 77- Large synthetic dataset for training foundation models (FWHM:2.2 pixels)\u003cbr\u003eCitationIf you use this data, please cite both this dataset and the original paper: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging, Biophysical Reports","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
<resource xmlns="http://datacite.org/schema/kernel-4" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.4/metadata.xsd">
  <identifier identifierType="DOI">10.4121/DF559DC9-6C7F-4766-9C10-DFDB065AB06D</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Miyase Tekpinar</creatorName>
      <givenName>Miyase</givenName>
      <familyName>Tekpinar</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0000-0003-0937-225X</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Jelle Komen</creatorName>
      <givenName>Jelle</givenName>
      <familyName>Komen</familyName>
    </creator>
    <creator>
      <creatorName>Hana Valenta</creatorName>
      <givenName>Hana</givenName>
      <familyName>Valenta</familyName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Ran Huo</creatorName>
      <givenName>Ran</givenName>
      <familyName>Huo</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0009-0003-2843-1355</nameIdentifier>
    </creator>
    <creator>
      <creatorName nameType="Personal">Klarinda de Zwaan</creatorName>
      <givenName>Klarinda</givenName>
      <familyName>de Zwaan</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0009-0002-7410-8375</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Peter Dedecker</creatorName>
      <givenName>Peter</givenName>
      <familyName>Dedecker</familyName>
    </creator>
    <creator>
      <creatorName nameType="Personal">Nergis Tömen</creatorName>
      <givenName>Nergis</givenName>
      <familyName>Tömen</familyName>
      <nameIdentifier nameIdentifierScheme="https://orcid.org/">0000-0003-3916-1859</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Kristin Grußmayer</creatorName>
      <givenName>Kristin</givenName>
      <familyName>Grußmayer</familyName>
    </creator>
  </creators>
  <titles>
    <title>Raw data underlying the publication: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging</title>
  </titles>
  <publisher>4TU.ResearchData</publisher>
  <publicationYear>2026</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
  <subjects>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0699">Other Biological Sciences</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0801">Artificial Intelligence and Image Processing</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0205">Optical Physics</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="1099">Other Technology</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="09">Engineering</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="02">Physical Sciences</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="10">Technology</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="08">Information and Computing Sciences</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="06">Biological Sciences</subject>
    <subject>fluctuation microscopy simulations</subject>
    <subject>foundation AI models</subject>
    <subject>convolutional gated recurrent unit (convGRU)</subject>
    <subject>fluorescence proteins, DNA-PAINT</subject>
    <subject>real-time imaging, live-cell, high-throughput</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">TU Delft, Department of Bionanoscience and Kavli Institute of Nanoscience, Grussmayer Lab</contributorName>
    </contributor>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision Lab</contributorName>
    </contributor>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">KU Leuven, Department of Chemistry, Dedecker Lab</contributorName>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2026-07-17</date>
  </dates>
  <language>en</language>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo">10.1101/2025.06.05.658028</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d.v1</relatedIdentifier>
  </relatedIdentifiers>
  <formats>
    <format>.zip, .txt, .tiff, . pt, .pth, .ts</format>
  </formats>
  <version>1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;RESURF DATA REPOSITORY:&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;This repository provides data which is used for our paper: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging (RESURF). &lt;/p&gt;&lt;p&gt;RESURF is a deep-learning based real-time SR imaging method. We used a recurrent neural network based model ( MISRGRU) and trained it using realistic fluorescence microscopy simulations. We simulated a large dataset containing imaging conditions of different SNRs and blinking conditions and used it to create multiple foundation models. We fine-tuned foundation models with smaller datasets ( either experimental or simulated). In this repository, we provide the complete dataset we used for training and testing as well as foundation models and fine-tuned models.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;1- Foundation Models, created by using large synthetic dataset (FWHM:2.2 pixels)&lt;/p&gt;&lt;p&gt;2- Live-cell Models, used in Figure 3-4-5-6 (FWHM:2.7 pixels)&lt;/p&gt;&lt;p&gt;3- Simulations&lt;/p&gt;&lt;p&gt;  Foundation_models_testdata: Synthetic test set used for testing the foundation models (Figure 1-2)&lt;/p&gt;&lt;p&gt;  Live_cell_simulations: Simulations with high-background imitating fluorescence protein conditions&lt;/p&gt;&lt;p&gt;  which is used for training live-cell models (transfer learning from foundation models)&lt;/p&gt;&lt;p&gt;4- Live-cell experiments used in Figure 3-4&lt;/p&gt;&lt;p&gt;5- DNA-PAINT models, used in Figure 7 (FWHM:3.8 pixels)&lt;/p&gt;&lt;p&gt;6- DNA PAINT experiments, used in Figure 7&lt;/p&gt;&lt;p&gt;7- Large synthetic dataset for training foundation models (FWHM:2.2 pixels)&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Citation&lt;/p&gt;&lt;p&gt;If you use this data, please cite both this dataset and the original paper: A Lightweight Deep Learning Framework for Fast, Real-Time Super-Resolution Fluctuation Imaging, Biophysical Reports&lt;/p&gt;</description>
  </descriptions>
</resource>","url":"https://data.4tu.nl/datasets/df559dc9-6c7f-4766-9c10-dfdb065ab06d","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":1,"citationsOverTime":[{"year":"2026","total":1}],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-07-17T11:45:52.000Z","registered":"2026-07-17T11:45:53.000Z","published":"2026","updated":"2026-07-17T11:45:53.000Z"},"relationships":{"client":{"data":{"id":"delft.data4tu","type":"clients"}},"provider":{"data":{"id":"scpr","type":"providers"}},"media":{"data":{"id":"10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d","type":"media"}},"references":{"data":[]},"citations":{"data":[{"id":"10.1101/2025.06.05.658028","type":"dois"}]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[{"id":"10.4121/df559dc9-6c7f-4766-9c10-dfdb065ab06d.v1","type":"dois"}]}}}}