cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/d8q141
title: Genotyping_pegard_etal_Alfalfa.txt

description: Genotyping data of 1012 accessions by GBS: 395 cultivated accessions used to study the genetic structure and 617 accessions representing part of the genetic material of 5 breeders. The GBS on alfalfa has been conducted with a couple of enzymes PstI-MseI and the reads were aligned on the reference genome published by Chen et al., 2020. The SNP are in rows and the accessions are in columns. The first column is the SNP name (chromosome_position), the second column is the allele used as Reference and alt the allele is used as Alternative. The others columns are the allele frequencies of the alternative allele for each accession.
DOI: 10.57745/fmrm4m
title: Phenotyping_pegard_etal_Alfalfa.txt

description: This dataset contains the phenotyping values for phenology : Fall (autumn) dormancy and flowering date. A plant trial of 400 accessions was established in two locations for phenotyping, the first one in France at the research unit (URP3F) of INRAE (Lusignan: 46° 23’ 60’’ N, 0° 4’ 48’’ E) and the second one in Serbia at the research unit of IFVCNS (Novi Sad: 45° 15’ 0’’ N, 19° 51’ 0’’ E). The trial of Lusignan was damaged by a storm in May 2018 and a new trial was sown in August 2018. Some of the genetic resources did not have enough seeds, so only 387 among the original 400 accessions were established. The trials were composed of 440 plots, 44 columns and 10 rows, in an augmented block design with four incomplete blocks. The micro-environmental plot effect was subtracted from the observed phenotype to obtain a spatially adjusted phenotype. For the repeated accessions, we calculated an accession mean of the spatially adjusted phenotypes for each trait. Please read the paper for more details. The first column is the accession EUCLEG_ID and the other columns are the adjusted phenotype. The traits are related to Flowering date (FD) and autumn dormancy depending on different measurements in autumn: Dormancy (D), Dry Matter Yield (F-DMY), plant height (PH), Speed elongation (SE) for two years 2019 (X19.X) and 2020 (X20.X) in two locations : Lusignan (.L) in France and Novi Sad (.N) in Serbia. F-DMY without letter or number is the Dry Matter Yield adjusted for year and location effects.
DOI: 10.57745/ftjyex
title: PlantMaterial.txt

description: This dataset summarizes each accession: their EUCLEG_ID, their name, their origin, and the provider of the seeds. The explanation of the sample status column can be found at https://www.genesys-pgr.org/descriptors/e2f86832-65f1-4204-8a81-d81f2c9307a2.
DOI: 10.11588/data/wzdkt6/ozhhsl
title: Fig4.tab

description: :unav
DOI: 10.11588/data/wzdkt6/b12xrc
title: Fig3.tab

description: :unav
DOI: 10.11588/data/wzdkt6/rhgbby
title: FigS6.tab

description: :unav
DOI: 10.11588/data/wzdkt6/otryso
title: FigS5.xlsx

description: :unav
DOI: 10.11588/data/wzdkt6/7oeokh
title: Fig1.tab

description: :unav
DOI: 10.11588/data/wzdkt6/ded50s
title: Fig2.xlsx

description: :unav
DOI: 10.11588/data/wzdkt6/ojxops
title: Fig6.tab

description: :unav
DOI: 10.11588/data/wzdkt6/qancnk
title: Fig5.tab

description: :unav
DOI: 10.11588/data/wzdkt6/ghadty
title: FigS1.tab

description: :unav
DOI: 10.11588/data/wzdkt6/dsijp2
title: FigS11.tab

description: :unav
DOI: 10.11588/data/wzdkt6
title: Allosteric Activation of Vinculin by Talin [data]

description: The talin-vinculin axis is a key mechanosensing component of cellular focal adhesions. How talin and vinculin respond to forces and regulate one another remains unclear. By combining single molecule magnetic tweezer experiments, Molecular Dynamics simulations, actin bundling assays, and adhesion assembly experiments in live cells, we here discover a two-ways allosteric network within vinculin as a regulator of the talin-vinculin interaction. We directly observe a maturation process of vinculin upon talin binding which reinforces the binding to talin at a rate of 0.03 s−1. This allosteric transition can compete with force-induced dissociation of vinculin from talin only at 7-10 pN. Mimicking the allosteric activation by mutation yields a vinculin molecule that bundles actin and localizes to focal adhesions in a force-independent manner. Hence, the allosteric switch confines talin-vinculin interactions and focal adhesion build-up to intermediate force levels. The ‘allosteric vinculin mutant’ is a valuable molecular tool to further dissect the mechanical and biochemical signalling circuits at focal adhesions and elsewhere.
DOI: 10.11588/data/wzdkt6/9wdqgv
title: FigS8.tab

description: :unav
DOI: 10.6084/m9.figshare.23274704
title: Impact probability

description: Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015)
DOI: 10.6084/m9.figshare.23274704.v1
title: Impact probability

description: Impact-based probabilistic modeling of hydro-morphological processes in China (1985-2015)
DOI: 10.5281/zenodo.7993137
title: Assessing the conservation status of Chinese freshwater fish using deep learning

description: The lack of information on the extinction risk of most species is a fundamental challenge in prioritizing conservation strategies and bending the curve of current biodiversity decline. Machine learning methods have shown promising potential to fill this gap, but their applicability remains to be validated at different taxa (especially aquatic species) and spatial scales. We assessed the extinction risk of 1,162 freshwater fish species in China that have not yet been included in the latest IUCN Red List using multiple neural network algorithms based on datasets of species occurrences, biological traits, phylogeny, and relevant environmental layers. The best deep learning models dramatically improved the assessment coverage from 29.9% (496 species) to 93.2–93.9% (1,545–1,557 species) of the whole fauna with an accuracy of 95.4–99.0%. By combining our prediction results with the IUCN Red List, we found that 23.8–26.5% (394–440 species) of Chinese freshwater fishes were identified as possibly threatened species, which is roughly four times the IUCN assessment. Newly assessed species and threatened species were mainly from the orders Cypriniformes (prediction added: 837–846 species; final threatened: 325–350 species), Siluriformes (113–122; 28–37) and Perciformes (74–76; 18–25). The increase in threatened species richness based on predictions was led by the upper reaches of the Pearl and Yangtze. Overall, our findings suggest that deep learning algorithms can provide robust and time-saving assessments of extinction risk for entire freshwater fish fauna on a large national scale, thereby facilitating relevant conservation prioritization.
DOI: 10.5281/zenodo.7993136
title: Assessing the conservation status of Chinese freshwater fish using deep learning

description: The lack of information on the extinction risk of most species is a fundamental challenge in prioritizing conservation strategies and bending the curve of current biodiversity decline. Machine learning methods have shown promising potential to fill this gap, but their applicability remains to be validated at different taxa (especially aquatic species) and spatial scales. We assessed the extinction risk of 1,162 freshwater fish species in China that have not yet been included in the latest IUCN Red List using multiple neural network algorithms based on datasets of species occurrences, biological traits, phylogeny, and relevant environmental layers. The best deep learning models dramatically improved the assessment coverage from 29.9% (496 species) to 93.2–93.9% (1,545–1,557 species) of the whole fauna with an accuracy of 95.4–99.0%. By combining our prediction results with the IUCN Red List, we found that 23.8–26.5% (394–440 species) of Chinese freshwater fishes were identified as possibly threatened species, which is roughly four times the IUCN assessment. Newly assessed species and threatened species were mainly from the orders Cypriniformes (prediction added: 837–846 species; final threatened: 325–350 species), Siluriformes (113–122; 28–37) and Perciformes (74–76; 18–25). The increase in threatened species richness based on predictions was led by the upper reaches of the Pearl and Yangtze. Overall, our findings suggest that deep learning algorithms can provide robust and time-saving assessments of extinction risk for entire freshwater fish fauna on a large national scale, thereby facilitating relevant conservation prioritization.
DOI: 10.34740/kaggle/dsv/5823528
title: Dota 2 Pro League Matches 2023
DOI: 10.5281/zenodo.15212021
title: Measuring the refractive index of materials with very low reflection coefficients at millimeter wavelengths

description: The dataset consists of one-port S-parameter measurements collected with the Keysight Technologies E8364C Programmable Network Analyzer (PNA) with an OML, Inc., V12VNA2-T/R millimeter wave Vector Network Analyzer (VNA) extender to operate in E band (60 - 90 GHz).   A CustomMicrowave, Inc., Model RCH012R conical horn atnetnna is connected by waveguide to the VNA extender.  An aperture in a layer of radar absorbing material (RAM) is used to reduce the radiated beam width; the absorbing material is RAM IS-005A manufactured by TDK RF Solutions, Inc. The materials are measured in free space placed atop a 30.5 x 30.5 cm aluminum plate.  The assembly was set atop a breadboard attached to a Thorlabs Model MLJ 050 motorized vertical translation stage with 0.1 micrometer resolution.  The S-parameter data is calibrated.  The calibration used the aluminum plate for the short and offset short calibration standards, and a similarly-sized piece of RAM for the load.  Calibration was done for two reference planes at the front and back of the samples.  The one port S11 reflection coefficient frequency spectrum is transformed into the time domain locally in the VNA using Keysight's time domain option.  

Measurements were performed on cross-linked polyethylene (EPE 3 lb)  of density 48 kg/m^3, and closed cell polystyrene (EPS 3 lb and EOS 1 lb) of density 48 kg/m^3 and 16 kg/m^3.  The materials were manufactured with the nominal thickness of 7.5 cm, and cut to the dimensions of the metal plate.  The derivation of the refractive index of the foams is described in Smith, P. R., J. C. Weatherall, and Barber, J., Measuring the refractive index of foam materials with very low reflection coefficients at milimeter wavelengths, in SPIE Proceedings 13028, pp. 235-241 (June 2024).  Application is to the N42.59 standard for evaluating imaging performance of active millimeter wave Advanced Imaging Technology systems for security screening.

s1p data file naming conventions:

t2 Material time data in vicinity of reference plane: Time  & Real & Imagt3 Material time data vicinity of reflection peak: Time & Real & Imagwo Time data without (wo) material

Ancillary data:Lx = 7.573 +/- 0.026   (* ave EPS 1b thickness cm *)Lx = 7.593 +/- 0.040   (* ave EPS 3 lb thickness  *)
DOI: 10.5281/zenodo.15212022
title: Measuring the refractive index of materials with very low reflection coefficients at millimeter wavelengths

description: The dataset consists of one-port S-parameter measurements collected with the Keysight Technologies E8364C Programmable Network Analyzer (PNA) with an OML, Inc., V12VNA2-T/R millimeter wave Vector Network Analyzer (VNA) extender to operate in E band (60 - 90 GHz).   A CustomMicrowave, Inc., Model RCH012R conical horn atnetnna is connected by waveguide to the VNA extender.  An aperture in a layer of radar absorbing material (RAM) is used to reduce the radiated beam width; the absorbing material is RAM IS-005A manufactured by TDK RF Solutions, Inc. The materials are measured in free space placed atop a 30.5 x 30.5 cm aluminum plate.  The assembly was set atop a breadboard attached to a Thorlabs Model MLJ 050 motorized vertical translation stage with 0.1 micrometer resolution.  The S-parameter data is calibrated.  The calibration used the aluminum plate for the short and offset short calibration standards, and a similarly-sized piece of RAM for the load.  Calibration was done for two reference planes at the front and back of the samples.  The one port S11 reflection coefficient frequency spectrum is transformed into the time domain locally in the VNA using Keysight's time domain option.  

Measurements were performed on cross-linked polyethylene (EPE 3 lb)  of density 48 kg/m^3, and closed cell polystyrene (EPS 3 lb and EOS 1 lb) of density 48 kg/m^3 and 16 kg/m^3.  The materials were manufactured with the nominal thickness of 7.5 cm, and cut to the dimensions of the metal plate.  The derivation of the refractive index of the foams is described in Smith, P. R., J. C. Weatherall, and Barber, J., Measuring the refractive index of foam materials with very low reflection coefficients at milimeter wavelengths, in SPIE Proceedings 13028, pp. 235-241 (June 2024).  Application is to the N42.59 standard for evaluating imaging performance of active millimeter wave Advanced Imaging Technology systems for security screening.

s1p data file naming conventions:

t2 Material time data in vicinity of reference plane: Time  & Real & Imagt3 Material time data vicinity of reflection peak: Time & Real & Imagwo Time data without (wo) material

Ancillary data:Lx = 7.573 +/- 0.026   (* ave EPS 1b thickness cm *)Lx = 7.593 +/- 0.040   (* ave EPS 3 lb thickness  *)
DOI: 10.17632/6b4h27t3bj.1
title: AutoNaVIT : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - JSON Compatible

description: AutoNaVIT is a curated segmentation label dataset developed to support research in autonomous navigation, scene understanding, and deep learning-based object segmentation. This release contains only the annotation labels corresponding to high-resolution frames extracted from a recorded driving sequence at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be released in Version 2 of the dataset shortly.

The dataset offers manually annotated, pixel-accurate segmentation masks for three key classes relevant to autonomous vehicle navigation:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were generated using Roboflow, ensuring high fidelity and consistency for real-world autonomous driving applications in urban and semi-urban environments.

Data Capture Specifications:

Source imagery was recorded using a Sony IMX890 sensor with the following specifications:

Sensor Size: 1/1.56", 50 MP

Lens: 6P, ƒ/1.8, 24mm equivalent, 1.0 µm pixels

Features: OIS, PDAF autofocus

Duration: 4 min 11 sec video

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support:

AutoNaVIT annotations are provided in standard JSON format, enabling direct compatibility with the following 7 models:

Florence-2 OD

Paligemma

COCO

COCO – Segmentation

CreateML

COCO – MMDetection

SAM2

As the format adheres to common JSON standards, the labels can be easily adapted for use with other models or frameworks that support JSON-based annotations.

Benchmark Results:

To validate the dataset’s effectiveness, a YOLOv8-based segmentation model was trained using the full dataset (images + annotations). The model achieved:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These results demonstrate the dataset’s reliability for training and evaluating segmentation models in autonomous vehicle systems.

Disclaimer and Attribution Requirement:
By accessing or using this dataset, users agree to the following terms under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0):

Usage is permitted only for non-commercial academic and research purposes.

Proper attribution must state:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This must be included in all publications, presentations, or other dissemination formats.

Redistribution, commercial use, modification, or public hosting of the dataset in any form is prohibited without explicit written permission from VIT-C.

Use of this dataset implies acceptance of these terms. All rights not expressly granted are reserved by VIT-C.
DOI: 10.17632/nh645b8ds8.1
title: AutoNaVIT : Vision-Based Path and Obstacle Segmentation Dataset for Autonomous Driving - TXT Compatible

description: AutoNaVIT is a meticulously curated dataset developed to assist research in autonomous navigation, scene understanding, and deep learning-based object segmentation. This release contains only the annotation labels in TXT format corresponding to high-resolution frames extracted from a recorded driving sequence at Vellore Institute of Technology – Chennai Campus (VIT-C). The corresponding images will be made available in Version 2 of the dataset soon.

The dataset features manually annotated bounding boxes and labels for three essential classes critical for autonomous vehicle navigation:

Kerb – 1,377 instances

Obstacle – 258 instances

Path – 532 instances

All annotations were created using Roboflow, ensuring high fidelity and consistency, which is vital for real-world autonomous driving applications in both urban and semi-urban environments.

Data Capture Specifications
Source imagery was recorded using a Sony IMX890 sensor with the following specifications:

Sensor Size: 1/1.56", 50 MP

Lens: 6P, ƒ/1.8, 24mm equivalent, 1.0 µm pixels

Features: OIS (Optical Image Stabilization), PDAF autofocus

Video Duration: 4 min 11 sec

Frame Rate: 2 FPS

Total Annotated Frames: 504

Format Compatibility and Model Support
AutoNaVIT annotations are provided in standard TXT format, enabling direct compatibility with the following 13 models:

yolokeras

yolov4pytorch

darknet

yolov5-obb

yolov8-obb

imt-yolov6

yolov4scaled

yolov5pytorch

yolov7pytorch

yolov8

yolov9

yolov11

yolov12

As the dataset adheres to standard YOLO TXT annotations, it can easily be adapted for other models or frameworks that support TXT-based annotations.

Benchmark Results
To evaluate the dataset’s performance, a YOLOv8-based segmentation model was trained on the complete dataset (images + annotations). The model achieved:

Mean Average Precision (mAP): 96.5%

Precision: 92.2%

Recall: 94.4%

These results confirm the dataset's high utility and reliability in training segmentation models for autonomous vehicle perception systems.

Disclaimer and Attribution Requirement
By accessing or using this dataset, users agree to the terms outlined under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0):

Usage is permitted solely for non-commercial academic and research purposes.

Proper attribution must be given, stating:
“Dataset courtesy of Vellore Institute of Technology – Chennai Campus.”
This acknowledgment must be included in all forms of publication, presentation, or dissemination of work utilizing this dataset.

Redistribution, commercial use, modification, or public hosting of the dataset is prohibited without explicit written permission from VIT-C.

Use of this dataset implies acceptance of these terms. All rights not explicitly granted are reserved by VIT-C.
DOI: 10.25549/impa-m46367
title: Haffner, Hugo.
title: Haffner, Hugo.
DOI: 10.25549/impa-m46371
title: Zimmer, Woldemar.
title: Zimmer, Woldemar.
DOI: 10.25549/impa-m46377
title: Tischhauser, Georg Johann.
title: Tischhauser, Georg Johann.
DOI: 10.25549/impa-m46379
title: Pfenning, Erich.
title: Pfenning, Erich.
DOI: 10.25549/impa-m46382
title: Keck, Jakob.
title: Keck, Jakob.
DOI: 10.25549/impa-m46396
title: Grandel, Johann Leonhard.
title: Grandel, Johann Leonhard.
DOI: 10.25549/impa-m46403
title: Vischer, Mattheus Karl.
title: Vischer, Mattheus Karl.
DOI: 10.25549/impa-m46427
title: Tröster, Georg Heinrich.
title: Tröster, Georg Heinrich.
DOI: 10.25549/impa-m46430
title: Oberlerchner, Adolf.
title: Oberlerchner, Adolf.
DOI: 10.25549/impa-m46437
title: Bachmann, Albert.
title: Bachmann, Albert.
DOI: 10.25549/impa-m46439
title: Autenrieth, Theodor.
title: Autenrieth, Theodor.

description: Stamped: "H. Jäggle, Tübingen" [front]
DOI: 10.25549/impa-m46440
title: Haas, Johannes [Ghana, USA]
title: Haas, Johannes [Ghana, USA]
DOI: 10.25549/impa-m45812
title: Sengle, Paul.
title: Sengle, Paul.
DOI: 10.25549/impa-m45814
title: Gebhardt, Christian.
title: Gebhardt, Christian.

description: Format information questionable.
DOI: 10.25549/impa-m45815
title: Schwarz, Gottfried.
title: Schwarz, Gottfried.

description: Format information questionable.
DOI: 10.25549/impa-m45817
title: Hey, Friedrich.
title: Hey, Friedrich.

description: Format information questionable.
DOI: 10.25549/impa-m45818
title: Kaiser, Wilhelm.
title: Kaiser, Wilhelm.
DOI: 10.25549/impa-m45819
title: Stokes, William [1867].
title: Stokes, William [1867].
DOI: 10.25549/impa-m45821
title: Büchner, Robert.
title: Büchner, Robert.
DOI: 10.25549/impa-m45822
title: Siegle, Wilhelm Gottlob.
title: Siegle, Wilhelm Gottlob.
DOI: 10.25549/impa-m45823
title: Fischer, Gottlieb.
title: Fischer, Gottlieb.
DOI: 10.25549/impa-m45824
title: Heinecken, Friedli Hermann Franz.
title: Heinecken, Friedli Hermann Franz.
DOI: 10.25549/impa-m45825
title: Wertz, Jakob Wilhelm.
title: Wertz, Jakob Wilhelm.
DOI: 10.25549/impa-m45826
title: Hermann, David.
title: Hermann, David.
DOI: 10.25549/impa-m45827
title: Lienhard, Philipp.
title: Lienhard, Philipp.
DOI: 10.25549/impa-m45828
title: Chapuis, François.
title: Chapuis, François.
DOI: 10.25549/impa-m45829
title: Schosser, Alfons.
title: Schosser, Alfons.
DOI: 10.25549/impa-m45830
title: Rohde, Hermann.
title: Rohde, Hermann.
DOI: 10.25549/impa-m45831
title: Fahrenberger, Johannes.
title: Fahrenberger, Johannes.
DOI: 10.25549/impa-m45832
title: Schilling, Robert.
title: Schilling, Robert.
DOI: 10.25549/impa-m45835
title: Hartenstein, Eugen.
title: Hartenstein, Eugen.
DOI: 10.25549/impa-m45837
title: Nonnenmacher, Gottlob Wilhelm.
title: Nonnenmacher, Gottlob Wilhelm.
DOI: 10.25549/impa-m45839
title: Dahl, Friedrich Joh. Joachim Dietrich.
title: Dahl, Friedrich Joh. Joachim Dietrich.
DOI: 10.25549/impa-m45840
title: Martin, Hermann.
title: Martin, Hermann.
DOI: 10.25549/impa-m45843
title: Rath, Wilhelm Gottlob.
title: Rath, Wilhelm Gottlob.
DOI: 10.25549/impa-m45844
title: Müller, Johann Jakob [1869].
title: Müller, Johann Jakob [1869].
DOI: 10.25549/impa-m45845
title: Lutz, Friedrich.
title: Lutz, Friedrich.
DOI: 10.25549/impa-m45847
title: Wieber, Karl.
title: Wieber, Karl.
DOI: 10.25549/impa-m45848
title: Dettmann, Johann.
title: Dettmann, Johann.
DOI: 10.25549/impa-m45849
title: Howe, Wilhelm Mich..
title: Howe, Wilhelm Mich..
DOI: 10.25549/impa-m45850
title: Niehaus, Hermann.
title: Niehaus, Hermann.
DOI: 10.25549/impa-m45851
title: Mühlemann, Jakob.
title: Mühlemann, Jakob.
DOI: 10.25549/impa-m45852
title: Gantenbein, Bartholome.
title: Gantenbein, Bartholome.
DOI: 10.25549/impa-m45853
title: Lüthy, Ernst.
title: Lüthy, Ernst.
DOI: 10.25549/impa-m45854
title: Müller, Friedrich [China].
title: Müller, Friedrich [China].
DOI: 10.25549/impa-m45855
title: Jost, Friedrich Adolf.
title: Jost, Friedrich Adolf.
DOI: 10.25549/impa-m45856
title: Wolpert, Hermann.
title: Wolpert, Hermann.
DOI: 10.25549/impa-m45857
title: Höhn, Karl Rudolf.
title: Höhn, Karl Rudolf.
DOI: 10.25549/impa-m45858
title: Kirchner, Adolf.
title: Kirchner, Adolf.
DOI: 10.25549/impa-m45859
title: Blümle, Friedrich.
title: Blümle, Friedrich.
DOI: 10.25549/impa-m45861
title: Hies, Wilhelm.
title: Hies, Wilhelm.
DOI: 10.25549/impa-m45864
title: Ziegele, Hugo Ferdinand Ernst Julius.
title: Ziegele, Hugo Ferdinand Ernst Julius.
DOI: 10.25549/impa-m45865
title: Nusser, Martin.
title: Nusser, Martin.
DOI: 10.25549/impa-m45867
title: Weber, Ludwig.
title: Weber, Ludwig.
DOI: 10.25549/impa-m45869
title: Pfisterer, Andreas.
title: Pfisterer, Andreas.
DOI: 10.25549/impa-m45870
title: Schürle, Georg.
title: Schürle, Georg.
DOI: 10.25549/impa-m45871
title: Schulte, Franz.
title: Schulte, Franz.
DOI: 10.25549/impa-m45872
title: Biehle, Emil Georg.
title: Biehle, Emil Georg.
DOI: 10.25549/impa-m45873
title: Gutmann, Karl Christ..
title: Gutmann, Karl Christ..
DOI: 10.25549/impa-m45874
title: Renschler, Gotthilf Christoph.
title: Renschler, Gotthilf Christoph.