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170 results for “dosimetry”
Selected data(s) from : Femtosecond direct laser writing of silver clusters in phosphate glasses for x-ray spatially-resolved dosimetry
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1.</strong> Microscopy fluorescence image of ARGOi glass sample (excitation at 365 nm) of laser-inscribed structures for the different writing irradiances at two different depths: (<strong>a</strong>) structures at 150 µm below the glass front surface, (<strong>b</strong>) structures at 550 µm below the glass front surface, and at 150 µm from the glass rear surface. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> (<strong>a</strong>) Transparent color before irradiation (ARGO glass sample), (<strong>b</strong>) yellow color after X-ray irradiation with 222 Gy (ARGO* glass sample). <strong>(Only picture)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>) Absorption spectra of the ARGO and ARGO* glass sample after various X-ray doses and the difference absorption coefficient spectrum for 222 Gy vs. pristine. (<strong>b</strong>) Fit of the radiation-induced spectrum (difference between 222 Gy and pristine) considering Gaussian energy contributions for ARGO and ARGO*. (<strong>c</strong>) Absorption spectra for the GPN and GPN* glasses for X-ray doses from 5 mGy to 3 kGy [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>d</strong>) The difference absorption coefficient spectra between different doses conditions for GPN and GPN* [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_2022-03-03_V01. <strong>Figure 3</strong></li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_Datas_2022-03-03_V01. Datas : <strong>wavelength, effective absorption coefficient (cm-1)</strong></li> </ol> <p>- <strong>Figure 4.</strong> Micro-luminescence of GPN* glass performed on the optically polished glass side: (<strong>a</strong>) integrated fluorescence intensity at different depths, (<strong>b</strong>) normalized spectrum evolution with depth for the 500 Gy dose [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_2022-03-03_V01. Figure 4</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_Datas_2022-03-03_V01. Datas</li> </ol> <p>- <strong>Figure 5.</strong> Estimated depth-dependent profiles in absolute values of the linear absorption coefficient at 405 nm. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_2022-03-03_V01. Figure 5</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_Datas_2022-03-03_V01. Datas : <strong>sample depth (mm) ; scaled linear absorption coefficient profile at 405 nm (mm-1)</strong></li> </ol> <p>- <strong>Figure 6.</strong> (<strong>a</strong>) X-ray energy spectra simulated by SpekPy for each irradiation facility, normalized by integral. (<strong>b</strong>) Geant4-simulated dose inside each sample, normalized by the surface dose; filled areas show uncertainties at 95% confidence. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_2022-03-03_V01. Figure 6</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_Datas_2022-03-03_V01. Datas : <strong>ARGO 100KV_dose ; GPN-20KV_dose ; GPN-32KV_dose</strong></li> </ol> <p>- <strong>Figure 7.</strong> Radio-photoluminescence measurement of the GPNi* glass for the inscribed structure [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_2022-03-03_V01. Figure 7</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_Datas_2022-03-03_V01. Datas : <strong>wavelength ; relative intensity a.u.</strong></li> </ol> <p>- <strong>Figure 8.</strong> Normalized RPL spectra excited at 325 nm: (<strong>a</strong>) for the ARGO (pristine—right axis) and ARGO* (X-ray irradiation at 222 Gy—left axis) glasses collected around 150 µm below the surface, (<strong>b</strong>,<strong>c</strong>) for the highest DLW irradiance structure for ARGOi and ARGOi* in the front- and the rear-inscribed surfaces, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_2022-03-03_V01. Figure 8</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_Datas_2022-03-03_V01. Datas : <strong>inscribed glass...</strong></li> </ol> <p>- <strong>Figure 9.</strong> (<strong>a</strong>) Differential linear absorption coefficient of the laser-inscribed structures (11 TW/cm<sup>2</sup>) for the two planes after irradiation at 222 Gy X-ray dose in the ARGOi* glass sample. (<strong>b</strong>) Average differential absorption of the inscribed structures for all DLW irradiance (as from <a href="https://www.mdpi.com/2227-9040/10/3/110/htm#fig_body_display_chemosensors-10-00110-f009">Figure 9</a>a). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_2022-03-03_V01. Figure 9</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_Datas_2022-03-03_V01. Datas : <strong>integrated differential linear absoprtion percentage ; irradiance (TW/cm2)</strong></li> </ol> <p>- <strong>Figure 10.</strong> (<strong>a</strong>) Phase image under white light illumination of the laser inscribed structure (11 TW/cm<sup>2</sup>) before irradiation. (<strong>b</strong>) Optical path difference determined from the phase image. (<strong>c</strong>) The refractive index modification Δ<em>n</em> as a function of laser irradiance before/after 222 Gy-dose for the two planes in ARGOi, ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_2022-03-03_V01. Figure 10</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_Datas_2022-03-03_V01. Datas : <strong>refractive index modification ; irradiance (TW/cm2), Error bar</strong></li> </ol> <p><strong>- Figure 11.</strong> Comparison between calculated and measured Δ<em>n</em>ˆ after irradiation for a decrease in the initial value of <em>N</em><em>α</em>3 by 0.48%: (<strong>a</strong>,<strong>c</strong>) the real part Δ<em>n</em> for the front and rear surfaces, respectively; (<strong>b</strong>,<strong>d</strong>) their imaginary counterparts Δ<em>κ</em>, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_2022-03-03_V01. Figure 11</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_Datas_2022-03-03_V01. Datas : <strong>rear surface...</strong></li> </ol> <p><strong>- Figure 12.</strong> Integrated measure of the amplitude of fluorescence intensity for the different laser irradiance before and after 222 Gy-dose for the two planes in ARGOi and ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_2022-03-03_V01. Figure 12</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_Datas_2022-03-03_V01. Datas : <strong>integrated measure of the amplitude of fluorescence intensity ; Irradiance (TW/cm2) ; Error bar </strong></li> </ol> <p><strong>- Figure 13.</strong> (<strong>a</strong>) Composite FLIM and fluorescence intensity microscopy images of the laser-induced structure (11 TW/cm<sup>2</sup>) before and after irradiation for an emission at 425 nm from the front surface; the color-code represents the mean lifetime obtained by FAST-FLIM algorithm (color scale from 0 to 31 ns); inset: luminescence intensity only (grey-scale from 0 to 45 counts). (<strong>b</strong>) Same composite FLIM and luminescence intensity images for an emission at 510 nm. (<strong>c</strong>) Luminescence decays in arbitrary units for the emission at 425 nm of the same structure before and after irradiation for the two surfaces, and fitting curves thereof using three exponential decay functions. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_2022-03-03_V01. Figure 13</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_Datas_2022-03-03_V01. Datas : <strong>fluorescence intensity (arbitrary units) ; time (ms)</strong></li> </ol> <p>- <strong>Figure 14.</strong> Dose-dependent evolution of the amplitude ratio of extracted spectral bands for (<strong>a</strong>) the GPNi* glass sample for DLW irradiance of 13.4 TW/cm<sup>2</sup> at 160 µm below the glass surface, (<strong>b</strong>) the ARGOi and ARGOi* glass sample for DLW irradiance of 11 TW/cm<sup>2</sup> at 550 µm below the glass surface (rear surface). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_2022-03-03_V01. Figure 14</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_Datas_2022-03-03_V01. Datas : <strong>ratio of amplitudes of spectral bands ; doses (gy)</strong>.</li> </ol>
Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols
<p>A database of organ dose coefficients using MC simulations for a clinically representative virtual population of adult and pediatric XCAT patient models over an expanded set of 21 exam protocols in digital tomosynthesis. </p>
Dataset for "Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy"
<p>Dataset for paper:</p> <p>Gómez F, Gonzalez-Castaño DM, Fernández NG,Pardo-Montero J, Schüller A, Gasparini A,Vanreusel V, Verellen D, Felici G, Kranzer R, Paz-Martín J.</p> <p>Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy.</p> <p>Med Phys.2022;49:4705–4714.</p> <p><a href="https://doi.org/10.1002/mp.15668">https://doi.org/10.1002/mp.15668</a></p>
Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"
<p>Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"</p> <p>Daniela Poppinga <em>et al</em> 2021 <em>Biomed. Phys. Eng. Express</em> 7 015012</p> <p>https://doi.org/10.1088/2057-1976/abcae5</p> <p> </p>
Study to Evaluate Safety and Dosimetry of Lutathera in Adolescent Patients With GEP-NETs and PPGLs
ClinicalTrials.gov study NCT04711135. IPD Sharing: YES. Countries: 5. Publications: 1.
Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling
<p>This is the data repository for the PLOS ONE Manuscript: "Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling". This repository contains the following data:</p> <p>1. "State-and-Subdivision-Boundary-Files.Edited-for-ArcMap-10.4.KML-Format.zip":</p> <p>This file contains modified U.S. state and sub-division boundary files [in KML format], which can be imported into ArcMap using its KMLtoLayer function. These files have been modified to prevent sub-division naming issues that we encountered when importing boundary data into ArcMap: A) State sub-divisions with identical names are considered a single sub-division by ArcMap (corrected by adding a letter after each sub-division of the same name, i.e. CenterA, CenterB, etc), and; B) ArcMap would only identify the sub-division by its first word if sub-division name contained spaces (corrected by converting all spaces into dashes). </p> <p>2. "HPAC-Plumes.Processed.zip" and "HPAC-Plumes.Unprocessed.zip":</p> <p>These files contain HPAC plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. We provide "processed" and "unprocessed" HPAC plume data files. The "unprocessed" HPAC plume data is provided in its original XML format, which cannot be imported into ArcMap directly. The "processed" HPAC plumes are provided in tab-delimited X,Y,Z format (Latitude, Longitude, and Dose). We have also added a "0 cGy" contour in the "processed" plumes (surrounding the HPAC plume), as the presence of unirradiated data points adjacent to the plume was found to be crucial for accurate kriging, since these points served as boundaries for kriging.</p> <p>3. "Final-Derived-Plumes.Data-Points.zip":</p> <p>This file contains geostatistically-derived plume coordinate (WGS1984) and dose (in cGy) values for all scenarios discussed in the manuscript. Data is in comma-delimited format (Latitude, Longitude, and Dose). Data points consist of a set of initial coordinates generated at random locations within each Census sub-division using the ArcMap tool, ‘CreateRandomPoints_management’, and subsequent points generated by densification (the geostatistical procedure that targets and localizes an additional small cohort of irradiated individuals to mitigate uncertainty in environmental measurements). These data points were assigned radiation level values corresponding to the adjacent outer HPAC contour by a script comparing each sample with its location within the HPAC plume of the same scenario.</p> <p>4. “Intermediate-Derived-Plumes.Data-Points.zip”</p> <p>This archive contains coordinate data (WGS1984) and dose values (in cGy) for all intermediary steps of plume development (using our geostatistical method) for all scenarios. Like (3), the data is comma-delimited (Latitude, Longitude, and Dose), and were assigned radiation level values by a script comparing sampling locations with the location of the HPAC plume of the same scenario. Scenario replicate folders contains text files for each iteration step of the plume derivation process, including a file containing just the initial random sampling (“Iteration-1”), a file containing initial sampling and sampling locations selected by the first densification step (“Iteration-2”), a file containing initial sampling and sampling locations selected by the first and second densification steps (“Iteration-3”), and so on.</p> <p>This archive also contains a Table (“Progression-of-New-Densification-Selected-Sampling-Locations-For-All-Scenarios.xslx”) which provides a categorical breakdown of how many unique densification-selected sampling locations occur within the irradiated region (i.e. overlap the HPAC plume) for each iteration of all scenario replicates. The fraction of irradiated to unirradiated sampling locations varies among each scenario and individual replicates for the same scenario. Our analysis shows that these results depend on the population densities and exact topography of the HPAC plume which is different among each scenario.</p> <p>5. "Geostatistical-Sampling-Project.All-Scripts.zip"</p> <p>This archive contains all programs required for this project. This includes Python scripts meant to be run within the ArcMap software environment (for random point generation and data extraction), and Perl scripts used to process HPAC and U.S. State and Sub-division boundary files, and to assign radiation values to sample locations based on a modified HPAC plume. A java program, “CompareReplicates.jar”, compares the overlapping areas between a pair of polygons that overlap one other using the ArcMap software environment, and requires access to the ArcGIS Runtime SDK (<a href="https://developers.arcgis.com/arcgis-runtime/">https://developers.arcgis.com/arcgis-runtime/</a>).</p>
Personal Online Dosimetry Using Computational Methods: The PODIUM Project and the Future of Active Dosimetry
<p>Individual monitoring of workers exposed to external ionizing radiation is essential to allow application of the ALARA principle and follow up of the official dose limits. However, large uncertainties still exist in personal dosimetry. Also, many practical problems exist for personal dosimetry, with many dosemeters getting lost and the reluctance of many workers to wear one or more dosemeters.</p> <p>Most legal dosimetry is done with passive dosemeters, which are analyzed after the wearing period in an accredited lab. Active dosemeters are also widely used, although mostly only for ALARA purposes or for specific exposure situations. Although the active dosemeters are dosimetrically and technically at least equivalent to passive dosemeters, their higher cost limits their use as only legal dosemeters.</p> <p>In an attempt of reinventing dosimetry by using the modern evolutions in simulations, artificial intelligence and computer vision, the PODIUM project was set up. PODIUM was a short feasibility project, funded by the EC CONCERT programme.</p> <p>The objective of the PODIUM project was to improve personal dosimetry by an innovative approach: the development of an online dosimetry application based on computer simulations without the use of physical dosemeters. Operational quantities, protection quantities and radiosensitive organ doses (e.g. eye lens, brain, heart, extremities) can be calculated based on the use of modern technology such as personal tracking devices, flexible individualized phantoms and scanning of geometry set-up. When combined with fast simulation codes, the aim was to perform personal dosimetry in real-time.</p> <p>We applied and validated the methodology for two situations where improvements in dosimetry are urgently needed: neutron workplaces and interventional radiology. Several validation and test measurements were done in different hospitals, and in 2 workplace fields with significant neutron exposure. Personal doses could be calculated within acceptable simulation times, just based on captured movements of the workers and information of the radiation fields. These doses agreed with the results from physical dosemeters within the standard uncertainties that are accepted in personal dosimetry.</p> <p>This PODIUM dosimetry method can also be used to visualize the radiation in near real time. This will increase awareness of radiation protection among workers and will improve the application of the ALARA principle, and it can also be used in training modules. The use of neural networks and big data will help in further reducing simulation time, making real time simulations and dosimetry without physical dosemeters possible in the near future.</p>
AAPM TG 236 Report 236: Recommendations on volume-image-based treatment planning, dosimetry, and quality management for HDR intracavitary brachytherapy: Part I breast
<p>This is a zipped file for Appendix A.1 DVH validation using reference DCIOM datasets for AAPM TG report 236: Part I. Intracavitary breast brachytherapy. One can download and unzip the file. These are CT images and a structure file in DICOM format to validate the DVH information in your HDR brachytherapy treatment planning system either Varian BrachyVision or Elekta Oncentra. One can import all the DICOM files into your brachytherapy TPS and compute the dose. One can follow the instruction described in detail in Appendix A.1 of the AAPM TG 236: Part I report.</p>
177Lu-DOTATATE Modified Delivery Based on Individualized Dosimetry
ClinicalTrials.gov study NCT06395402. IPD Sharing: YES. Countries: 1. Publications: 4.
A Study to Evaluate the Safety, Biodistribution, Internal Radiation Dosimetry, and Effective Dose of DaTSCAN™ Ioflupane (123I) Injection in Chinese Healthy Volunteers.
ClinicalTrials.gov study NCT04564092. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Translation of Generator-Produced PET Tracer for Myocardial Perfusion Imaging-Dosimetry Group
ClinicalTrials.gov study NCT05280782. IPD Sharing: NO. Countries: 1. Publications: 1.
Tc99m-MAA Bronchial Artery Injection During Bronchial Embolization for Pulmonary Mass Induced Hemoptysis for Dosimetry Planning
ClinicalTrials.gov study NCT04105283. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study to Evaluate the Efficacy, Safety, Pharmacokinetics and Dosimetry of [177Lu]Lu-PSMA-617 in Chinese Adult Male Patients With Progressive PSMA-Positive mCRPC
ClinicalTrials.gov study NCT05670106. IPD Sharing: YES. Countries: 1. Publications: 0.
A Study to Evaluate Safety, Tolerability, Dosimetry, and Preliminary Efficacy of the HER2 Directed Radioligand CAM-H2 in Patients With Advanced/Metastatic HER2-Positive Breast, Gastric, and Gastro-Eso
ClinicalTrials.gov study NCT04467515. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Dataset Sachindra et al: SPECT/CT imaging, biodistribution and radiation dosimetry of a 177Lu-DOTA-integrin αvβ6 cystine knot peptide in a pancreatic cancer xenograft model
<p>Numerical data for the manuscript 'SPECT/CT imaging, biodistribution and radiation dosimetry of a 177Lu-DOTA-integrin αvβ6 cystine knot peptide in a pancreatic cancer xenograft model'</p>
Supporting Material: Proof-of-principle of 3D-printed track-end detectors for dosimetry in proton therapy
<p><strong>Project description and contents of the submitted manuscript:</strong></p> <p><strong>Background: </strong>Dosimetric equipment in particle therapy (PT) is associated with high costs. There is a lack of versatile, tissue-equivalent detectors suitable for in-vivo dosimetry. Faraday-cup (FC) type detectors are sensitive to stopped protons, i.e. to trackends. They experience a renaissance in PT as they can cope with high dose rates. Owing to their simple functional principle, production of FC could benefit from the dynamic technological developments in additive manufacturing of sensors.</p> <p><strong>Purpose: </strong>To build FC-type detectors for PT by standard 3D-printing. This study seeks to build an integrating, single-channel FC for replacement of a traditional FC and a 2×2 array of FC elements indicating the feasibility of a spatially resolving detector.</p> <p><strong>Methods: </strong>Samples of FCs were produced with a dual-extruder 3D-printer with polylactic-acid filaments, which contained graphite in the conductive parts of the detector. Production was optimizied in terms of materials and printing temperature. Samples were characterized by electrical tests and non-destructive 3D x-ray imaging. Beam tests were conducted at a clinical PT machine.</p> <p><strong>Results: </strong>Operational FC-type detectors for proton fields were printed. The detected charge of the single-channel FC corresponded qualitatively to the one of a traditional FC. A 2 × 2 FC array was fabricated in a single run. There was a linear relationship between the response of the individual FC elements and the machine output.</p> <p><strong>Conclusions: </strong>3D-printing is a viable method for producing low-cost, tissue-equivalent, FC-type detectors for PT. They could potentially be used as track-end detectors in anthropomorphic phantoms.</p> <h3>Shared files:</h3> <p>2x2_FC_matrix: design CAD files of FC array</p> <p>Single_channel_FC: design CAD files of simplified, single-channel FC</p> <p>CT_matrix: high-resolution x-ray CT image set of the 2x2 FC array</p> <p>Measured_values_for_MedPhys_technical_note: Excel sheet of the data presented in the paper</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset: "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"
<p>Dataset for the paper entitled "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"</p>
Alpharadin™ (Radium-223 Chloride) Safety and Dosimetry With HRPC That Has Metastasized to the Skeleton
ClinicalTrials.gov study NCT00748046. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Safety and Dosimetry of a New Radiotracer to Detect Misfolded SOD1 Associated With Amyotrophic Lateral Sclerosis
ClinicalTrials.gov study NCT05974579. IPD Sharing: NO. Countries: 1. Publications: 0.
Dosimetry Guided PRRT With 177Lu-DOTATATE in Children and Adolescents
ClinicalTrials.gov study NCT03923257. IPD Sharing: NO. Countries: 1. Publications: 3.
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.