FDAT Research Data Repository (Universität Tübingen)
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Genealogy of a member of the ṭāyefa Eskandary descended from Karbalāʾ Bārānī, Kerman Province, Iran
No full textThe data shows the genealogy of an elderly villager from the village Bagh-e Borj, located in the mountains of Kerman (Southeast Iran). All villagers belong to the ṭāyefa Eskandery. ṭāyefa is an indigenous term that could be translated to English as 'tribe' although this term is, of course, a bit problematic. The Eskanderies live endogamous and believe to have a common descent from one forefather - all Eskanderies are descendants of a common ancestor who is known among them under the name Karbalāʾ Bārānī. This can be seen in the genealogy: The villager traces his descent via his patriline back to the common forefather Karbalāʾ Bārānī. Karbalāʾ is said to have come to the mountains of Bagh-e Borj roughly 300-350 years ago and most Eskanderies can trace their own (patri-)line back to this mythological figure. The genealogy is not complete: Occasionally the names of ancestors were unknown and female ancestors occur 'only' from the fourth generation after Karbalāʾ on. This corresponds with the survey method – the genealogy was recorded in an interview with the villager and, hence, stems from his memory and not from a written archive (such an archive doesn't exist). This is not a disadvantage: memory is not that much concerned with completeness or factuality, actually not even with the past. It is concerned with establishing social cohesion in the present and that is what this genealogy does: the Eskanderies' idea of being a group is significantly strengthened by the belief in a common descent.
The surname Eskandery was taken by the ṭāyefa members in 1924, when citizens of Iran had to take a family name by law. Eskandery was and stil is a popular name that many citizens choose as their family name and so did the members of the respective ṭāyefa
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_00.nii.gz (CT baseline image) |--- c6f057b865_BL_mask_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_00.nii.gz (CT follow-up image, first body region) |--- c6f057b865_FU_img_01.nii.gz (CT follow-up image, second body region; if available) |--- ... |--- targetsTr |--- c6f057b865_FU_mask_00.nii.gz (CT follow-up lesion mask of first body region, integer mask) |--- c6f057b865_FU_mask_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
cog_backpropagated: Lesion center of gravity in baseline image as 3D pixel coordinates backpropagated from follow-up image using conventional registration (not available for all lesions; only when lesion is newly appearing in follow-up)
img_id_bl: baseline image ID (either 0, 1 or 2)
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, 1 or 2)
lesion_type: Anatomical lesion location ('Adrenals', 'CNS', 'Heart', 'Kidney', 'Liver', 'Lung', 'Lymph node', 'Others', 'Skeleton', 'Soft tissue / Skin', 'Spleen', 'unclear')
topology_class: Lesion topology between baseline to follow-up ('DISAPPEARING', 'MERGING', 'NEWLYAPPEARING', 'UNCHANGED')
merged_into: Lesion ID in the follow-up for 'MERGING' cases
volume_bl: Lesion volume [mm3] in baseline image
volume_fu: Lesion volume [mm3] in follow-up image
target_lesion: Boolean (True/False) if reader identified a target lesion
use_for_challenge: Boolean (True/False) if data was included in autoPET/CT IV challenge
linking_unclear: Reader states that lesion linking is unclear between baseline and follow-up
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
Korpusstudien zu Adjektiv-Nomen-Komposita im Deutschen
No full text"Die Dateien enthalten die annotierten Korpusbelege von zwei Korpusstudien zu Adjektiv-Nomen-Komposita im Deutschen. Beim verwendeten Korpus handelt es sich um das Webkorpus DECOW16B. Die Korpusstudien befassen sich exemplarisch mit den Adjektiven "schön" und "schnell".
Die Korpusstudie zu "schön" erforscht Wortbildungsprodukte, die aus dem Adjektiv "schön" und einem deverbalen Nomen bestehen, wie etwa "Schönredner" oder "Schönspieler". Im Zentrum der Studie steht die Frage, welche Konzepte hinter solchen Kombinationen stehen.
Die Korpusstudie zu "schnell" untersucht die Kombinatorik des Adjektivs innerhalb von Komposita (Beispiel: "Schnellboot") und Phrasen (Beispiel: "schneller Griff"). Dazu wurden die semantischen Typen der beteiligten Nomina annotiert." Research carried out in work package A01 of the SFB 833.Die zwei Studien sind Teil der folgenden Dissertation: Natascha Elxnath (2025): Komposita als Mittel der Konzeptbenennung: Untersuchungen zur Semantik und Pragmatik von Adjektiv-Nomen-Komposita im Deutschen. Universität Tübingen. http://dx.doi.org/10.15496/publikation-113062. 
Efficacy of a mathematical structuring competence intervention for talented students: A randomized controlled field trial
No full textBackground: Mathematical structuring competence (MSC)—the ability to recognize, apply, and build patterns and structures—is a crucial component of mathematics education and an essential characteristic of mathematical talent. However, systematic approaches to fostering MSC, especially for talent promotion, are still lacking.
Aims: We evaluated the efficacy of a newly developed intervention for enhancing talented students' MSC as a primary outcome and their arithmetic performance and mathematics-related motivational dispositions as secondary outcomes. The intervention was conducted online and involved two core components: inquiry-based learning and mathematically rich pattern tasks.
Sample: We collected data from 104 talented 2nd to 5th graders (32 girls) participating in an extracurricular STEM enrichment program.
Methods: We used a randomized controlled field trial with repeated measures and a treated control group to evaluate the efficacy of the intervention. Multiple linear regression analyses were calculated to test for intervention effects.
Results: Students in the intervention group developed more sophisticated MSC than those in the control group (b = 0.46, p < .001). No differential intervention effects on MSC were found, indicating that all students, regardless of their prior knowledge, fluid intelligence, gender, or grade level, benefited equally from the intervention. The intervention did not significantly affect arithmetic performance or motivational dispositions.
Conclusions: The study showed that talented students' MSC can be promoted by combining inquiry-based learning with mathematically rich pattern tasks. Thus, our study enriches previous studies on MSC-related competencies in regular school settings and ultimately enhances knowledge on how to promote talented students' MSC
Peru, Benin, Haiti – German-Language Literature and the Transatlantic World of the 18th Century
No full textAt first glance, this sequence is puzzling: Peru, Benin, and Haiti? What does German-language literature have to do with these countries? – At second and third glance, however, these geographical references prove to have a surprising presence. Looking beyond the canon, one discovers a German-language literature that knows much about the world—one that was global before it passed through the 19th-century literary-historical sorting machine programmed for the nation
Cortical sensory aging is layer-specific
No full textWe employed a unique approach and acquired layer-specific structural and functional magnetic resonance imaging (fMRI) data using 7T-MRI of the 2 (which was not certified by peer review) is the author/funder. All rights reserved. bioRxiv preprint doi: https://doi.org/10.1101/2023.12.01.567841; this version posted July 15, 2024. The subject of this preprint is the primary somatosensory cortex (SI) of healthy younger and older adults together with behavioral assessments. We also investigated an individual with unilateral congenital arm loss to test for the effect of reduced sensory input on the SI layer architecture.The segregation of processes into cortical layers is a convergent feature in animal evolution. However, how changes in the cortical layer architecture interact with sensory system function and dysfunction remains unclear. We conducted functional and structural layer-specific in-vivo 7T-MRI of the primary somatosensory cortex in two cohorts of healthy younger and older adults. Input layer IV is enlarged and more myelinated in older adults, and associated with extended sensory input signals. Age-related cortical thinning is driven by deep layers and accompanied by increased myelination, but there is no clear evidence for reduced inhibition. Calcium imaging and histology in younger and older mice reveal increased sensory-evoked neuronal activity accompanied by increased parvalbumin expression as a potential inhibitory balance, with dynamic changes in layer-specific myelination across age groups. Using multimodal imaging, we demonstrate that middle and deep layers show specific sensitivity to aging across species
Organic Residue Analysis in Bronze Age/Iron Age pottery, soils, and adobes from the Southern Iberian Peninsula
No full textThe dataset contains the data associated with organic residue analysis (ORA), including chromatograms in .csv and .MzML formats, alongside Excel tables detailing sampling information. This repository entry pertains to the ORA analysis, conducted in the laboratory of the University of Tübingen under the supervision of Prof. Dr. Maxime Rageot (currently at the University of Bonn).
The primary objective of this ORA analysis was to identify traces of Phoenician presence or evidence of trade with the Eastern Mediterranean, as well as to examine variations in pottery usage and the nature of commodities processed before and after the Phoenicians' arrival in the Iberian Peninsula (approximately the 9th century BC).Analysis
The analysis was performed by Gas Chromatography (GC) and GC-Mass Spectrometry (GC-MS) using an Agilent Technologies 7890B GC System series chromatograph including Agilent Technologies Capillary Flow-Technology Three-Way Splitter Kit coupled to an Agilent Technologies 5977A MSD and FID. The analyses were carried out using helium as a carrier gas, with a split/splitless injection system (Gerstel Multi-Purpose-Sampler and Gerstel Cold-Injection-System 4), operating in the splitless mode with a purge flow of 3.0ml min–1 and a constant pressure at the head of the column of 8.6667 psi. Samples were analysed using an Agilent J&W DB-5HT-column (15m × 0.32mm i.d.; 0.1μm film thickness) and divided in two equal parts using 0.18mm non-coated, deactivated silica capillary columns (0.66m splitter-column to FID/ 1.52m splitter-column to MSD) with the Three-Way Splitter Kit. The inlet temperature was ramped from 30°C to 240°C at 12°C s-1 (held isothermally for 5min) and then increased to 350°C at 12°C s-1 (held isothermally for 10min). The temperature of the oven was set at 50°C for 1min followed by an increase to 100°C at 15°C min–1, then to 240°C at 6°C min–1 and to 350°C at 10°C min–1 (held isothermally for 20min). Mass spectra were acquired using electron ionization at 70 eV and obtained by scanning between m/z 50–950 in 1.562s. The interface and the ion source temperatures were 300°C and 280°C, respectively. The temperature of the FID detector was fixed at 340°C. Mass spectra were matched against the National Institute of Standards and Technology (NIST) library, 2014 edition
Tombstone - Verzeichnis gelöschter Datensätze
No full textAll but a few research data sets of the Tübingen Archive of Languages Resources (TALAR) have now been migrated to FDAT, the institutional research data repository of the University of Tübingen. There are a few datasets, however, that were not migrated. A full list of those "dead" datasets is given below.
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Fast alle Forschungsdaten aus dem Tübinger Archive of Language Resources (TALAR) wurden nun nach FDAT, dem institutionellen Forschungsdaten-Repositorium.der Universität Tübingen, migriert. Es gibt jedoch einige Datensätze, die nicht migriert wurden. Eine vollständige Liste dieser "toten" Datensätze ist unten aufgeführt.
Deleted datasets/gelöschte Datensätze:
https://hdl.handle.net/11022/0000-0000-2CBE-A
https://hdl.handle.net/11022/0000-0000-2D0D-1
https://hdl.handle.net/11022/0000-0000-16B8-8
https://hdl.handle.net/11022/0000-0000-1B07-B
https://hdl.handle.net/11022/0000-0000-206D-2
https://hdl.handle.net/11022/0000-0007-C5A5-0
https://hdl.handle.net/11022/0000-0007-C5A6-F
https://hdl.handle.net/11022/0000-0007-EB0D-3
https://hdl.handle.net/11022/0000-0007-F0AA-A
https://hdl.handle.net/11022/0000-0000-2C42-
PSMA-FDG-PET-CT-Lesions
No full textIntroduction
A publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT studies (900 patients) and 597 prostate-specific membrane antigen (PSMA)-PET/CT studies (378 patients) acquired between 2014 and 2022 were included. The FDG cohort comprises 501 patients diagnosed with histologically proven malignant melanoma, lymphoma, or lung cancer, along with 513 negative control patients. The PSMA cohort includes pre- and/or post-therapeutic PET/CT images of male individuals with prostate carcinoma, encompassing images with (537) and without PSMA-avid tumor lesions (60). Notably, the training datasets exhibit distinct age distributions: the FDG UKT cohort spans 570 male patients (mean age: 60; std: 16) and 444 female patients (mean age: 58; std: 16), whereas the PSMA LMU cohort tends to be older, with 378 male patients (mean age: 71; std: 8). Additionally, there are variations in imaging conditions between the FDG Tübingen and PSMA Munich cohorts, particularly regarding the types and number of PET/CT scanners utilized for acquisition. The PSMA Munich dataset was acquired using three different scanner types (Siemens Biograph 64-4R TruePoint, Siemens Biograph mCT Flow 20, and GE Discovery 690), whereas the FDG Tübingen dataset was acquired using a single scanner (Siemens Biograph mCT).
Structure and usage
The data is organized in the nnUNet structure:
|--- imagesTr |--- tracer_patient1_study1_0000.nii.gz (CT image resampled to PET) |--- tracer_patient1_study1_0001.nii.gz (PET image in SUV) |--- ... |--- labelsTr |--- tracer_patient1_study1.nii.gz (manual annotations of tumor lesions)
|--- dataset.json (nnUNet dataset description) |--- dataset_fingerprint.json (nnUNet dataset fingerprint)
|--- splits_final.json (reference 5fold split)
|--- psma_metadata.csv (metadata csv for psma) |--- fdg_metadata.csv (original metadata csv for fdg)
We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model: www.autopet.org
PET/CT acquisition protocol
FDG dataset: Patients fasted at least 6 h prior to the injection of approximately 350 MBq 18F-FDG. Whole-body PET/CT images were acquired using a Biograph mCT PET/CT scanner (Siemens, Healthcare GmbH, Erlangen, Germany) and were initiated approximately 60 min after intravenous tracer administration. Diagnostic CT scans of the neck, thorax, abdomen, and pelvis (200 reference mAs; 120 kV) were acquired 90 sec after intravenous injection of a contrast agent (90-120 ml Ultravist 370, Bayer AG) or without contrast agent (in case of existing contraindications). PET Images were reconstructed iteratively (three iterations, 21 subsets) with Gaussian post-reconstruction smoothing (2 mm full width at half-maximum). Slice thickness on contrast-enhanced CT was 2 or 3 mm.
PSMA dataset: Examinations were acquired on different PET/CT scanners (Siemens Biograph 64-4R TruePoint, Siemens Biograph mCT Flow 20, and GE Discovery 690). The imaging protocol mainly consisted of a diagnostic CT scan from the skull base to the mid-thigh using the following scan parameters: reference tube current exposure time product of 143 mAs (mean); tube voltage of 100kV or 120 kV for most cases, slice thickness of 3 mm for Biograph 64 and Biograph mCT, and 2.5 mm for GE Discovery 690 (except for 3 cases with 5 mm). Intravenous contrast enhancement was used in most studies (571), except for patients with contraindications (26).
The whole-body PSMA-PET scan was acquired on average around 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. For GE Discovery 690 the reconstruction process employed a VPFX algorithm with voxel size 2.73 mm × 2.73 mm × 3.27 mm, for Siemens Biograph mCT Flow 20 a PSF+TOF algorithm (2 iterations, 21 subsets) with voxel size 4.07 mm × 4.07 mm × 3.00 mm, and for Siemens Biograph 64-4R TruePoint a PSF algorithm (3 iterations, 21 subsets) with voxel size 4.07 mm × 4.07 mm × 5.00 mm.
Annotation
FDG PET/CT training and test data from UKT was annotated by a Radiologist with 10 years of experience in Hybrid Imaging and experience in machine learning research. FDG PET/CT test data from LMU was annotated by a radiologist with 8 years of experience in hybrid imaging. PSMA PET/CT training and test data from LMU as well as PSMA PET/CT test data from UKT was annotated by a single reader and reviewed by a radiologist with 5 years of experience in hybrid imaging.
The following annotation protocol was defined:Step 1: Identification of tracer-avid tumor lesions by visual assessment of PET and CT information together with the clinical examination reports.Step 2: Manual free-hand segmentation of identified lesions in axial slices
Going Native - Colonialism and the White Savior Trope in Denis Villeneuve's "Dune" Films
No full textDenis Villeneuve's "Dune" adaptations (2021, 2024) initially appear to follow a typical Hollywood-style White Savior narrative. However, a closer analysis reveals a more nuanced picture: the "Dune" series deconstructs this trope and offers a structural critique of colonialism