FDAT Research Data Repository (Universität Tübingen)
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A comprehensive database framework for the management and analysis of commingled human remains
No full textThis database template was specially designed for processing and analyzing commingled human remains. It offers the possibility to apply comprehensive analyses and recording methods and to adapt and extend them individually depending on the project. As the input options are based on special codes, several users can work on one project without any typing errors, duplication of data or loss of entries. Currently still for unburnt human remains, but for all types of burials.
The accompanying scripts are designed to help the user to quickly analyses the collected data
 
Soil information in Kurdistan region, Dohuk governorate (Iraq)
No full textThis data set consists of eight parts containing the maps, scripts, different parts containing the maps, scripts, raw data and predictions of soil information collected between 2017 and 2023 in the Dohuk governorate of the Kurdistan region of Iraq. This is the supplementary deposit to the paper Soil information in Kurdistan region, Dohuk governorate (Iraq).
A graph describes the entire workflow, and a .csv file contains a list of all the data produced. An additional list with all covariates used is also presented in a .csv file.
Additional visualisation of the final maps computed here can be found at https://mathias-bellat.shinyapps.io/Northern-Kurdistan-map
PSMA-PET-CT-Lesions
No full textIntroduction
We provide a large, annotated dataset of 597 whole-body PSMA-PET/CT studies from 378 male patients with suspected or diagnosed prostate carcinoma to support developing and benchmarking machine learning (ML) models for automated quantitative PET/CT analysis. Alongside the FDG-PET/CT dataset, this dataset addresses the scarcity of publicly available, high-quality annotated PET/CT data. The FDG and PSMA-PET/CT datasets were jointly provided as training data for developing ML models in the autoPET III and IV Grand Challenges for automated lesion segmentation in whole-body PET/CT.
Data Acquisition
Scans were conducted at LMU University Hospital, LMU Munich, between 2014 and 2022 using three clinical PET/CT scanners: Siemens Biograph mCT Flow 20, Siemens Biograph 64-4R TruePoint, and GE Discovery 690. 539 studies contain at least one PSMA-avid tumor lesion, 58 studies do not contain any PSMA-avid tumor lesion. The imaging protocol consisted of a diagnostic CT scan usually from the skull base to the mid-thigh with the following scan parameters: reference tube current exposure time product of 143 mAs (mean); tube voltage of 120 kV or 100 kV for most cases (range: [80, 140] kV), slice thickness of 2.5 - 5.0 mm (mean: 2.82 mm), and x-y resolution of mainly 0.98 mm. Intravenous contrast enhancement was used in most studies, except for patients with contraindications (26 studies). The whole-body PSMA-PET scan was acquired on average 74 minutes after intravenous injection of 246 MBq 18F-PSMA (mean, 369 studies) or 214 MBq 68Ga-PSMA (mean, 228 studies), respectively. The PET data was reconstructed with attenuation correction derived from corresponding CT data using standard, vendor-provided image reconstruction algorithms with a slice thickness ranging from 3.0 - 5.0 mm (mean: 3.49 mm) and x-y resolution ranging from 2.73 - 4.07 mm (mean: 3.56 mm).
Data Annotation
All PSMA-avid tumor lesions, including the primary tumor and/or all metastases, were manually segmented on the PET images by a single reader with 3 years of experience in hybrid imaging using dedicated software (mint Medical, Heidelberg, Germany) and validated by board-certified medical imaging experts with 4 years and >10 years of experience in hybrid imaging. Tumor lesions with significantly increased PSMA expression were segmented in 3D space by drawing circular VOIs, in which voxels with uptake values above a user-defined threshold were pre-segmented automatically and then manually corrected slice by slice, resulting in 3D binary segmentation masks.
Data Processing
For DICOM-to-NIfTI conversion, CT volumes were resampled to match the size and resolution of the corresponding PET volume, PET voxel values were normalized to standardized uptake values (SUV) based on body mass. In addition, patient metadata was extracted from imaging DICOM tags and saved in a CSV file: patient age at imaging in years, PET/CT manufacturer and model name, PET radionuclide, and use of CT contrast agent. Information on radionuclides and the use of CT contrast agents was visually reviewed and validated by a radiologist with 10 years of experience in hybrid imaging. Each study is uniquely identified by an anonymized case identifier number and the study date. The study date was shifted by a global patient-level offset, such that differences between the study dates of a patient are conserved.
Data structure
The NIfTI dataset is organized in nnU-Net structure.
|--- imagesTr |--- <tracer>_<patient_1>_<study_1>_0000.nii.gz (CT image resampled to PET) |--- <tracer>_<patient_1>_<study_1>_0001.nii.gz (PET image in SUV) |--- ...
|--- labelsTr |--- <tracer>_<patient_1>_<study_1>.nii.gz (SEG mask) |--- ...
|--- dataset.json (nnUNet dataset description) |--- dataset_fingerprint.json (nnUNet dataset fingerprint) |--- splits_final.json (reference 5-fold split) |--- psma_metadata.csv (metadata csv for psma)
Usage
The dataset can be used for training deep learning models for automated lesion segmentation in whole-body PET/CT: www.autopet.org
Version v2
Corrected segmentation masks in 5 studies from 3 patients. Lesion segmentations were updated in 3 studies, and added to 2 studies that previously had none.
Updated files:
labelsTr/psma_4da96443cf212c5f_2020-08-31.nii.gz
labelsTr/psma_4da96443cf212c5f_2022-01-15.nii.gz
labelsTr/psma_4da96443cf212c5f_2022-05-14.nii.gz
labelsTr/psma_5eb9920ce854b7a2_2019-03-29.nii.gz
labelsTr/psma_faa4c90c3d2d53a3_2019-08-10.nii.g
Soil mapping data for spatial modeling of soil organic carbon with meaningless predictors
No full textThis dataset contains data for the training of machine learning regression models from 668 hypothetical case studies in 334 study areas across Europe, a R script for data analysis and the random forest models that reproduce the spurious correlation between the spatial distribution of soil organic carbon (SOC) and meaningless predictors.
The 334 study areas are squared with 200 × 200 km and contain data from SOC from SoilGrids 2.0 (Poggio et al., 2021; https://doi.org/10.5194/soil-7-217-2021) as model outcome. 250 tiles are randomly distributed and 84 tiles are distributed regularly to account for bias towards areas covered by multiple randomly distributed tiles. The models are trained with 500 random samples and validated with another 1000 randomly selected samples from portrait images of researchers as independent covariates and SOC as outcome. The portrait images were reduced to greyscale with principal component analysis (PCA) and sRGB to linear luminance, which results in two series of 334 hypothetical case studies resulting in 668 hypothetical case studies in total..
The original portrait images are not included to protect personal rights and copyright. We thank Alexandre M. J.-C. Wadoux (https://doi.org/10.1111/ejss.12909) for providing the portrait images
Longitudinal-CT
No full textIntroduction
A publicly available dataset of annotated longitudinal Computed Tomography (CT) studies. The dataset comprises whole-body CT scans from 300 melanoma patients undergoing longitudinal imaging for therapy response assessment. Each patient has two imaging timepoints: a baseline staging scan and a follow-up scan acquired after therapy treatment. The dataset includes training data from a single site (UKT).
All CT examinations were acquired on state-of-the-art CT scanners using standardized protocols following international guidelines. The imaging protocol includes whole-body CT imaging, typically extending from the skull base to mid-thigh level, with possible extensions to include the entire body when clinically relevant (all data is defaced). The dataset provides anonymized NIfTI files of all CT scans along with manually annotated segmentation masks of malignant tumors, including primary tumors and metastases. The lesion center of gravity is provided for each individual lesion in the volume (baseline and follow-up scans). The tumors can change shape (progression or regression), split or merge, disappear (complete response) or newly appear (metastasis). Additionally, scripts for image processing and conversion to different file formats (DICOM, mha, hdf5) are available.
The dataset is designed to facilitate the development and evaluation of AI-based lesion detection and segmentation algorithms in longitudinal CT imaging for oncology applications. The inclusion of multiple imaging timepoints allows for the assessment of lesion progression and therapy response, providing a clinically realistic dataset for algorithm training and validation.
Structure and usage
Filenames start with a unique patient ID (10 digits). The data is organized in the following structure:
|--- inputsTr |--- c6f057b865.csv (lesion information for patient) |--- c6f057b865_BL_00.json (lesion center of gravity per lesion in baseline CT; Grand-Challenge JSON format) |--- c6f057b865_BL_img_BL_img_00.nii.gz (CT baseline image) |--- c6f057b865_BL_mask_BL_img_00.nii.gz (CT baseline lesion mask, integer mask) |--- c6f057b865_FU_00.json (lesion center of gravity per lesion in first follow-up CT; Grand-Challenge JSON format) |--- c6f057b865_FU_01.json (lesion center of gravity per lesion in second follow-up CT; Grand-Challenge JSON format; if available) |--- c6f057b865_FU_img_FU_img_00.nii.gz (CT follow-up image, first body region) |--- c6f057b865_FU_img_FU_img_01.nii.gz (CT follow-up image, second body region; if available) |--- ... |--- targetsTr |--- c6f057b865_FU_mask_FU_img_00.nii.gz (CT follow-up lesion mask of first body region, integer mask) |--- c6f057b865_FU_mask_FU_img_01.nii.gz (CT follow-up lesion mask of second body region, integer mask; if available) |--- ...
CSV file
The CSV file contains the following columns:
lesion_id: Continous ID count in the respective patient
cog_bl: Lesion center of gravity in baseline image as 3D pixel coordinates
img_id_bl: baseline image ID (either 0 or 1)
cog_propagated: Lesion center of gravity (as 3D pixel coordinates) propagated from baseline to follow-up scan using a conventional registration (not available for all lesions)
cog_fu: Lesion center of gravitiy in follow-up image as 3D pixel coordinates
img_id_fu: follow-up image ID (either 0 or 1)
lesion_type: Anatomical lesion location
We demonstrate how this dataset can be used for deep learning-based automated analysis of CT data and provide the trained deep learning model: www.autopet.org
CT acquisition protocol
All CT scans were acquired using Siemens CT scanners, including Siemens Sensation 64, Siemens SOMATOM Definition AS, Siemens SOMATOM Definition Flash, Siemens SOMATOM Force, and the Siemens Biograph128 PET/CT scanner. Patients were scanned using an in-house whole-body staging protocol in the supine position with arms raised above the head. The scanning procedure was performed during the portal-venous phase after the administration of body-weight-adapted contrast medium via the cubital vein.
To ensure consistent image quality, attenuation-based tube current modulation (CARE Dose, reference mAs 240) and a fixed tube voltage of 120 kV were applied. The following scan parameters were used across different CT scanners:
SOMATOM Force: Collimation 128 × 0.6 mm, rotation time 0.5 s, pitch 0.6.
Sensation64: Collimation 64 × 0.6 mm, rotation time 0.5 s, pitch 0.6.
SOMATOM Definition Flash: Collimation 128 × 0.6 mm, rotation time 0.5 s, pitch 1.0.
SOMATOM Definition AS: Collimation 64 × 0.6 mm, rotation time 0.5 s, pitch 0.6.
Biograph128: Collimation 128 × 0.6 mm, rotation time 0.5 s, pitch 0.8.
Slice thickness and increment were set to 3 mm, and image reconstruction was performed using a medium smooth kernel.
Annotation
All data were manually annotated by two experienced radiologists. To this end, tumor lesions were manually segmented on the CT image data using dedicated software.The following annotation protocol was defined:Step 1: Identification of tumor lesions by visual assessment of CT information together with the clinical examination reports.Step 2: Manual free-hand segmentation of identified lesions in axial slices.Step 3: Baseline and follow-up segmentations are viewed side-by-side to mark the matching lesions
Muscle-tendon mechanics resolve the trade-off between energy-efficient and robust locomotion
No full textThis study explores the trade-off between energy-efficient and robust locomotion in muscle-actuated locomotion. We found that muscle-tendon intrinsic mechanics resolve the trade-off between energy-efficient and robust locomotion by solely relying on intrinsic mechanics and a feedforward stimulation strategy that optimized for energy efficiency.
Corresponding author: Matthew Araz, [email protected] 
GermaNet: Ein lexikalisch-semantisches Wortnetz
No full textGermaNet ist ein lexikalisch-semantisches Wortnetz, das deutsche Nomina, Verben und Adjektive semantisch zueinander in Beziehung setzt, indem es lexikalische Einheiten, die dasselbe Konzept ausdrücken, in Synsets zusammenfasst und semantische Relationen zwischen diesen Synsets definiert. GermaNet hat viel mit dem Englischen WordNet® gemeinsam und kann als ein Online-Thesaurus oder als eine Lightweight-Ontologie betrachtet werden.GermaNet is a lexical-semantic net that relates German nouns, verbs, and adjectives semantically by grouping lexical units that express the same concept into synsets and by defining semantic relations between these synsets. GermaNet has much in common with the English WordNet® and can be viewed as an on-line thesaurus or a light-weight ontology.Release 20.0 contains:
179438 Synsets, 231500 Lexical units, 216517 Literals, 1.29 Lexical units per synset, 194367 Conceptual relations, 13602 Lexical relations (synonymy excluded), and 130901 Split compounds
PSMA-PET-CT-Lesions
No full textIntroduction
We provide a large, annotated dataset of 597 whole-body PSMA-PET/CT studies from 378 male patients with suspected or diagnosed prostate carcinoma to support developing and benchmarking machine learning (ML) models for automated quantitative PET/CT analysis. Alongside the FDG-PET/CT dataset, this dataset addresses the scarcity of publicly available, high-quality annotated PET/CT data. The FDG and PSMA-PET/CT datasets were jointly provided as training data for developing ML models in the autoPET III and IV Grand Challenges for automated lesion segmentation in whole-body PET/CT.
Data Acquisition
Scans were conducted at LMU University Hospital, LMU Munich, between 2014 and 2022 using three clinical PET/CT scanners: Siemens Biograph mCT Flow 20, Siemens Biograph 64-4R TruePoint, and GE Discovery 690. 537 studies contain at least one PSMA-avid tumor lesion, 60 studies do not contain any PSMA-avid tumor lesion. The imaging protocol consisted of a diagnostic CT scan usually from the skull base to the mid-thigh with the following scan parameters: reference tube current exposure time product of 143 mAs (mean); tube voltage of 120 kV or 100 kV for most cases (range: [80, 140] kV), slice thickness of 2.5 - 5.0 mm (mean: 2.82 mm), and x-y resolution of mainly 0.98 mm. Intravenous contrast enhancement was used in most studies, except for patients with contraindications (26 studies). The whole-body PSMA-PET scan was acquired on average 74 minutes after intravenous injection of 246 MBq 18F-PSMA (mean, 369 studies) or 214 MBq 68Ga-PSMA (mean, 228 studies), respectively. The PET data was reconstructed with attenuation correction derived from corresponding CT data using standard, vendor-provided image reconstruction algorithms with a slice thickness ranging from 3.0 - 5.0 mm (mean: 3.49 mm) and x-y resolution ranging from 2.73 - 4.07 mm (mean: 3.56 mm).
Data Annotation
All PSMA-avid tumor lesions, including the primary tumor and/or all metastases, were manually segmented on the PET images by a single reader with 3 years of experience in hybrid imaging using dedicated software (mint Medical, Heidelberg, Germany) and validated by board-certified medical imaging experts with 4 years and >10 years of experience in hybrid imaging. Tumor lesions with significantly increased PSMA expression were segmented in 3D space by drawing circular VOIs, in which voxels with uptake values above a user-defined threshold were pre-segmented automatically and then manually corrected slice by slice, resulting in 3D binary segmentation masks.
Data Processing
For DICOM-to-NIfTI conversion, CT volumes were resampled to match the size and resolution of the corresponding PET volume, PET voxel values were normalized to standardized uptake values (SUV) based on body mass. In addition, patient metadata was extracted from imaging DICOM tags and saved in a CSV file: patient age at imaging in years, PET/CT manufacturer and model name, PET radionuclide, and use of CT contrast agent. Information on radionuclides and the use of CT contrast agents was visually reviewed and validated by a radiologist with 10 years of experience in hybrid imaging. Each study is uniquely identified by an anonymized case identifier number and the study date. The study date was shifted by a global patient-level offset, such that differences between the study dates of a patient are conserved.
Data structure
The NIfTI dataset is organized in nnU-Net structure.
|--- imagesTr |--- <tracer>_<patient_1>_<study_1>_0000.nii.gz (CT image resampled to PET) |--- <tracer>_<patient_1>_<study_1>_0001.nii.gz (PET image in SUV) |--- ...
|--- labelsTr |--- <tracer>_<patient_1>_<study_1>.nii.gz (SEG mask) |--- ...
|--- dataset.json (nnUNet dataset description) |--- dataset_fingerprint.json (nnUNet dataset fingerprint) |--- splits_final.json (reference 5-fold split) |--- psma_metadata.csv (metadata csv for psma)
Usage
The dataset can be used for training deep learning models for automated lesion segmentation in whole-body PET/CT: www.autopet.org 
Guide and checklists for data submission and review in the FDAT community CRC 1070 - ResourceCultures
No full textThis collection of documents is a guideline for data submission and review within a FDAT community. It aims at researchers who intend to upload and publish own data sets as well as reviewers who review and accept the uploaded data sets
