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zenodo40/100

TROTS - The Radiotherapy Optimisation Test Set

The Radiotherapy Optimisation Test Set (TROTS) is an extensive set of problems originating from radiotherapy (radiation therapy) treatment planning. This dataset is created for 2 purposes: (1) to supply a large-scale dense dataset to measure performance and quality of mathematical solvers, and (2) to supply a dataset to investigate the multi-criteria optimisation and decision-making nature of the radiotherapy problem. The dataset contains 120 problems (patients), divided over 6 different treatment protocols/tumour types. Each problem contains numerical data, a configuration for the optimisation problem, and data required to visualise and interpret the results. The data is stored as HDF5 compatible Matlab files, and includes scripts to work with the dataset.   The set as present in this version is of date 13 May 2019. Updated versions of the Scripts and other extensions can be found at the following pages:   Persistent page with links: <a href="https://hdl.handle.net/1765/116520">Erasmus University Rotterdam Library</a> Mirror project page: <a href="http://www.sebastiaanbreedveld.nl/trots">TROTS Mirror</a> Main publication: <a href="https://dx.doi.org/10.1016/j.dib.2017.03.037">S. Breedveld & B. Heijmen, Data for TROTS - The Radiotherapy Optimisation Test Set, Data in Brief 12 (2017) 143-149 </a>

opencc-by-4.0Mar 2017View details →

zenodo40/100

Dataset for "Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy"

Dataset for paper: 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. Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy. Med Phys.2022;49:4705–4714. <a href="https://doi.org/10.1002/mp.15668">https://doi.org/10.1002/mp.15668</a>

opencc-by-4.0Apr 2022View details →

zenodo40/100

SynthRAD2023 Grand Challenge validation dataset: synthetizing computed tomography for radiotherapy

Version 1.1, updated on 2023-06-04 --> the task2_val.zip has been modified with a new cbct file for patient 2BA078. The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.7868169">10.5281/zenodo.7868169</a> and a detailed description is offered at <a href="https://doi.org/10.5281/zenodo.7260704">https://doi.org/10.5281/zenodo.7260704</a> in the "synthRAD2023_dataset_description.pdf". The input of the validation datasets for Task1 is in Task1_val.zip, while for Task2 in Task2_val.zip. After unzipping, each Task is organized according to the following folder structure: Task1_val.zip/ ├── Task1    ├── brain     ├── 1Bxxxx        ├── mr.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 1_brain_val.xlsx        ├── 1Bxxxx_val.png       └── ...      └── pelvis     ├── 1Pxxxx        ├── mr.nii.gz        ├── mask.nii.gz    ├── ... └── overview        ├── 1_pelvis_val.xlsx        ├── 1Pxxxx_val.png        └── .... Task2_val.zip/ ├──Task2    ├── brain     ├── 2Bxxxx        ├── cbct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 2_brain_val.xlsx        ├── 2Bxxxx_val.png       └── ...        └── pelvis     ├── 2Pxxxx        ├── cbct.nii.gz        ├── mask.nii.gz ├── ... └── overview       ├── 2_pelvis_val.xlsx       ├── 2Pxxxx_val.png       └── .... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: [Task]    [Anatomy]    [Center]    [PatientID] 1    B        A        001 In each patient folder, two files can be found:  <ul> <li> mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image </li> <li> mask.nii.gz: image containing a binary mask of the dilated patient outline  </li> </ul> For each task and anatomy, an overview folder is provided which contains the following files: <ul> <li> [task]_[anatomy]_val.xlsx: This file contains information about the image acquisition protocol for each patient. </li> <li> [task][anatomy][center][PatientID]_val.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data. </li> </ul> DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: <ul> <li> Radboud University Medical Center; </li> <li> University Medical Center Utrecht; </li> <li> University Medical Center Groningen. </li> </ul> For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the validation set. Validation <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 20 </td> <td> 0 </td> <td> 10 </td> <td> 30 </td> </tr> <tr> <td> Task 2 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> </tr> </tbody> </table> In total, for all tasks and anatomies combined, 120 image pairs are available in this dataset. This repository only contains the validation data. The training data is provided at: h<a href="https://doi.org/10.5281/zenodo.7260704">ttps://doi.org/10.5281/zenodo.7260704</a>. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: <ul> <li> Center A: <ul> <li> MRI: Philips Ingenia 1.5T/3.0T </li> <li> CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT </li> <li> CBCT: Elekta XVI </li> </ul> </li> <li> Center B: <ul> <li> MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T </li> <li> CT: Siemens SOMATOM Definition AS </li> <li> CBCT: IBA Proteus+ or Elekta XVI </li> </ul> </li> <li> Center C: <ul> <li> MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T </li> <li> CT: Philips Brilliance Big Bore </li> <li> CBCT: Elekta XVI </li> </ul> </li> </ul> For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: <ul> <li> Conversion from dicom to compressed nifti (nii.gz) </li> <li> Rigid registration between CT and MR/CBCT </li> <li> Anonymization (face removal, only for brain patients) </li> <li> Patient outline segmentation (provided as a binary mask) </li> <li> Crop MR/CBCT, CT and mask to remove background and reduce file sizes </li> </ul> The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: <ul> <li> UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. </li> <li> UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. </li> <li> Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. </li> </ul> CHALLENGE DESIGN The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746019">https://doi.org/10.5281/zenodo.7746019</a>.

opencc-by-nc-4.0May 2023View details →

ClinicalTrials.gov40/100

Targeted Radiotherapy in Androgen-suppressed Prostate Cancer Patients.

ClinicalTrials.gov study NCT03644303. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

zenodo36/100

Collected Colorimetric Microscopy (C-Microscopy) Images of Melanocytes and Melanoma 3D Spheroids Irradiated with Different Type of Proton Beam as Used in Proton Radiotherapy

Collected Colorimetric Microscopy (C-Microscopy) images, color calibrated (D65 illuminant), of melanocytes and melanoma 3D spheroids, irradiated with different type of proton beam as used in proton radiotherapy.   The data are supplement to: Martyna Durak-Kozica, Ewa Stępień, Jan Swakoń, Benedykt R. Jany, Kamil Kawoń, Damian Wróbel, Sebastian Kusyk, Małgorzata Grzesiak, Katarzyna Knapczyk-Stwora, Andrzej Wróbel, Joanna Chwiejand  Paweł Moskal, Short-term response of melanoma spheroids and melanocytes to FLASH proton therapy - colorimetric and FTIR microscopy study, Pol J Med Phys Eng 2024;30(4):263-268 (2024) <a href="https://doi.org/10.2478/pjmpe-2024-0031">https://doi.org/10.2478/pjmpe-2024-0031</a>   HEMA-Spheroids-C-Microscopy.zip - melanocytes 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 435.87 microns WM-Spheroids-C-Microscopy.zip - melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 1089.68 microns WM-Spheroids-Texture-C-Microscopy.zip - surface texture of melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 108.97 microns   Proton Beam Radiotherapy Irradiation Conditions: C - Control CC - Control minus 7days LP - conventional proton radiotherapy (CONV) final dose 3Gy (dose rate about 0.140 Gy/s) F - FLASH proton radiotherapy final dose 3Gy (dose rate >60 Gy/s) F20 - FLASH proton radiotherapy final dose 20Gy (dose rate >60 Gy/s) F40 - FLASH proton radiotherapy final dose 40Gy (dose rate >60 Gy/s)   The details about Colorimetric Microscopy (C-Microscopy) approach could be found in: Benedykt R. Jany, Quantifying Colors at Micrometer Scale by Colorimetric Microscopy (C-Microscopy) Approach, Micron 176, 103557 (2024) <a href="https://doi.org/10.1016/j.micron.2023.103557">https://doi.org/10.1016/j.micron.2023.103557</a>

opencc-by-4.0Sep 2024View details →

dryad36/100

Dietary nitrate supplementation prevents radiotherapy-induced xerostomia

Management of salivary gland hypofunction caused by irradiation (IR) therapy for head and neck cancer remains lack of effective treatments. Salivary glands, especially the parotid gland, actively uptake dietary nitrate and secrete it into saliva. Here, we investigated the effect of dietary nitrate on the prevention and treatment of IR-induced parotid gland hypofunction in miniature pigs, and elucidated the underlying mechanism in human parotid gland cells (hPGCs). We found that nitrate administration prevented IR-induced parotid gland damage in a dose-dependent manner, by maintaining the function of irradiated parotid gland tissue. Mechanically, Nitrate could increase sialin expression, a nitrate transporter expressed in the parotid gland, making the nitrate-sialin feedback loop that facilitates nitrate influx into cells for maintaining cell proliferation and inhibiting apoptosis. Nitrate enhanced cell proliferation via the epidermal growth factor receptor (EGFR)–protein kinase B (AKT)–mitogen-activated protein kinase (MAPK) signaling pathway in irradiated parotid gland tissue. Collectively, nitrate effectively prevented IR-induced xerostomia via the EGFR–AKT–MAPK signaling pathway. Dietary nitrate supplementation may provide a novel, safe, and effective way to resolve IR-induce xerostomia.

opencc-zeroOct 2021View details →

zenodo36/100

SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy

DATASET STRUCTURE The dataset can be downloaded from <a href="https://doi.org/10.5281/zenodo.7260705">https://doi.org/10.5281/zenodo.7260705</a> and a detailed description is offered at "synthRAD2023_dataset_description.pdf". The training datasets for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure: Task1.zip/ ├── Task1    ├── brain     ├── 1Bxxxx        ├── mr.nii.gz        ├── ct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 1_brain_train.xlsx        ├── 1Bxxxx_train.png       └── ...      └── pelvis     ├── 1Pxxxx        ├── mr.nii.gz        ├── ct.nii.gz        ├── mask.nii.gz    ├── ... └── overview        ├── 1_pelvis_train.xlsx        ├── 1Pxxxx_train.png        └── .... Task2.zip/ ├──Task2    ├── brain     ├── 2Bxxxx        ├── cbct.nii.gz        ├── ct.nii.gz        └── mask.nii.gz     ├── ... └── overview        ├── 2_brain_train.xlsx        ├── 2Bxxxx_train.png       └── ...        └── pelvis     ├── 2Pxxxx        ├── cbct.nii.gz        ├── ct.nii.gz        ├── mask.nii.gz ├── ... └── overview       ├── 2_pelvis_train.xlsx       ├── 2Pxxxx_train.png       └── .... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: [Task]    [Anatomy]    [Center]    [PatientID] 1    B        A        001 In each patient folder, three files can be found:  <ul> <li> ct.nii.gz: CT image  </li> <li> mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image </li> <li> mask.nii.gz:image containing a binary mask of the dilated patient outline  </li> </ul> For each task and anatomy, an overview folder is provided which contains the following files: <ul> <li> [task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient. </li> <li> [task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data. </li> </ul> DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: <ul> <li> Radboud University Medical Center </li> <li> University Medical Center Utrecht </li> <li> University Medical Center Groningen </li> </ul> For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the training set. Training <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 120 </td> <td> 0 </td> <td> 60 </td> <td> 180 </td> </tr> <tr> <td> Task 2 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> <td> 60 </td> <td> 60 </td> <td> 60 </td> <td> 180 </td> </tr> </tbody> </table> Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets. Validation <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 20 </td> <td> 0 </td> <td> 10 </td> <td> 30 </td> </tr> <tr> <td> Task 2 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> <td> 10 </td> <td> 10 </td> <td> 10 </td> <td> 30 </td> </tr> </tbody> </table> Testing <table> <tbody> <tr> <td> </td> <td> Brain </td> <td> Pelvis </td> </tr> <tr> <td> </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> <td> Center A </td> <td> Center B </td> <td> Center C </td> <td> Total </td> </tr> <tr> <td> Task 1 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 40 </td> <td> 0 </td> <td> 20 </td> <td> 60 </td> </tr> <tr> <td> Task 2 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> <td> 20 </td> <td> 20 </td> <td> 20 </td> <td> 60 </td> </tr> </tbody> </table> In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. This repository only contains the training data. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: <ul> <li> Center A: <ul> <li> MRI: Philips Ingenia 1.5T/3.0T </li> <li> CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT </li> <li> CBCT: Elekta XVI </li> </ul> </li> <li> Center B: <ul> <li> MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T </li> <li> CT: Siemens SOMATOM Definition AS </li> <li> CBCT: IBA Proteus+ or Elekta XVI </li> </ul> </li> <li> Center C: <ul> <li> MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T </li> <li> CT: Philips Brilliance Big Bore </li> <li> CBCT: Elekta XVI </li> </ul> </li> </ul> For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: <ul> <li> Conversion from dicom to compressed nifti (nii.gz) </li> <li> Rigid registration between CT and MR/CBCT </li> <li> Anonymization (face removal, only for brain patients) </li> <li> Patient outline segmentation (provided as a binary mask) </li> <li> Crop MR/CBCT, CT and mask to remove background and reduce file sizes </li> </ul> The code used to preprocess the images can be found at: <a href="https://github.com/SynthRAD2023/">https://github.com/SynthRAD2023/</a>. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: <ul> <li> UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. </li> <li> UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. </li> <li> Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. </li> </ul> CHALLENGE DESIGN The overall challenge design can be found at <a href="https://doi.org/10.5281/zenodo.7746020">https://doi.org/10.5281/zenodo.7746020</a>.

opencc-by-nc-4.0Dec 2022View details →

zenodo36/100

Transposable elements potentiate radiotherapy-induced cellular immune reactions via RIG-I-mediated virus-sensing pathways

Mass spectrometry-based proteomics source data.

opencc-by-4.0Dec 2022View details →

ClinicalTrials.gov36/100

A Study of DS-1001b in Patients With Chemotherapy- and Radiotherapy-Naive IDH1 Mutated WHO Grade II Glioma

ClinicalTrials.gov study NCT04458272. IPD Sharing: YES. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Individualized Stereotactic Body Radiotherapy of Liver Metastases

ClinicalTrials.gov study NCT01239381. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

A Trial of 15 Fraction vs 25 Fraction Pencil Beam Scanning Proton Radiotherapy After Mastectomy in Patients Requiring Regional Nodal Irradiation

ClinicalTrials.gov study NCT02783690. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

TARGeted Intraoperative radioTherapy With INTRABEAM as a Boost for Breast Cancer - A Quality Control Registry

ClinicalTrials.gov study NCT01440010. IPD Sharing: Not stated. Countries: 1. Publications: 25.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

MRI-Guided Lattice Extreme Ablative Dose Radiotherapy For Prostate Cancer

ClinicalTrials.gov study NCT01411319. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ClinicalTrials.gov36/100

Screening Trial of Nivolumab With Image Guided, Stereotactic Body Radiotherapy (SBRT) Versus Nivolumab Alone in Patients With Metastatic Head and Neck Squamous Cell Carcinoma (HNSCC)

ClinicalTrials.gov study NCT02684253. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Ipilimumab + Nivolumab w/Thoracic Radiotherapy for Extensive-Stage Small Cell Lung Cancer

ClinicalTrials.gov study NCT03043599. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

M3541 in Combination With Radiotherapy in Solid Tumors

ClinicalTrials.gov study NCT03225105. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Radiotherapy for Solid Tumor Spine Metastases

ClinicalTrials.gov study NCT01752036. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

The Detection Of Circulating Tumor Cells (CTC) In Patients With NSCLC Undergoing Definitive Radiotherapy Or Chemoradiotherapy

ClinicalTrials.gov study NCT02135679. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Normal Tissue Oxygenation Following Radiotherapy

ClinicalTrials.gov study NCT00677040. IPD Sharing: YES. Countries: 1. Publications: 13.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Study of Palliative Radiotherapy for Symptomatic Hepatocellular Carcinoma and Liver Metastases

ClinicalTrials.gov study NCT02511522. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record