cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-3878
title: MRI based perfusion of rats with 60% PVL

description: &lt;p&gt;The study investigates in vivo liver perfusion in rats. Perfusion of ligated and non-ligated liver lobes of 25 rats was monitored via MRI. Rats were subjected to 60% Portal Vein Ligation (PVL).&lt;/p&gt;

&lt;p&gt;Experimentally, the left stem of the portal vein (supplying Left Median (LML) and Left Lateral Lobe (LLL)) and the right stem to the Right Lobe (RL) were ligated.&lt;/p&gt;

&lt;b&gt;*Description of the Method and derived Units*&lt;/b&gt;

&lt;p&gt;An ASL MRI technique assessed Tissue perfusion in vivo on anaesthetized animals. This method employs selective inversion of magnetization within the imaging plane or global inversion across the entire animal. MR imaging was conducted using a rapid, single-shot EPI technique following this labelling process.&lt;/p&gt;

&lt;p&gt;To quantify tissue perfusion, which denotes the influx of water molecules from the bloodstream into the tissue and the subsequent exchange of tissue water, we measured the T1 recovery of the magnetization. This involved repeating selective and global inversions, followed by various delay times to capture the T1 recovery in both selective and global scenarios.&lt;/p&gt;

&lt;p&gt;Tissue perfusion was then determined by calculating the T1 difference between selective and global inversions and scaling the resultant values to mL/100 g/min, representing millilitres of blood per 100 grams of tissue per minute. This computation was executed on MRI images for each voxel. Subsequently, regions of interest (ROIs) were manually delineated on anatomical images for each liver lobe. The resultant single voxel perfusion values are stored in the *.mat files.&lt;/p&gt;

&lt;b&gt;*Description of the Dataset*&lt;/b&gt;

&lt;p&gt;The dataset consists of perfusion measurements for Regions of Interest (ROI) in each lobe. Perfusion values were obtained by collecting the voxel perfusion value for each ROI. The Dataset contains already curated representative perfusion values for each lobe. It can be used to obtain numeric perfusion values for 5 post operative days.&lt;/p&gt;

&lt;p&gt;This Dataset consists of:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;The raw data (zip file)&lt;/li&gt;
&lt;li&gt;A python script to convert the raw data to a tsv file&lt;/li&gt;
&lt;li&gt;The converted tsv file, containing the animal id, observation time, lobe and perfusion value&lt;/li&gt;
&lt;/ol&gt;
DOI: 10.18419/darus-3878/3
title: PVL_ROI_V2.1.zip
DOI: 10.18419/darus-3889/1
title: ikarus-docker-v0.4.tar.gz

description: Ikarus Docker image
DOI: 10.18419/darus-3889/2
title: ikarus-docs-v0.4.zip

description: Ikarus documentation
DOI: 10.18419/darus-3889
title: Ikarus v0.4

description: Ikarus is a C++-based library with Python bindings (&lt;a href="https://ikarus-project.github.io/v0.4/"&gt;link to documentation&lt;/a&gt;) built to solve partial differential equations with the finite element method. The dataset under this DOI contains the current release (v0.4) of the library.
&lt;br /&gt;&lt;br /&gt;
This release not only focuses on refactoring various interfaces but also introduces exciting features such as Python bindings, result evaluators, the Kirchhoff-Love shell element, added support for Clang 16, and more. For more details on the changes of this release refer to the repo file CHANGELOG.md. or go to &lt;a href="https://github.com/ikarus-project/ikarus/blob/main/CHANGELOG.md#release-v04-ganymede"&gt;GitHub&lt;/a&gt;.
&lt;br /&gt;&lt;br /&gt;
This dataset includes the Ikarus source code itself as a zip-file (ikarus-v0.4.zip).
&lt;br /&gt;&lt;br /&gt;
Additionally, this dataset comes with a Docker container (ikarus-docker-v0.4.tar.gz). It has Ikarus itself for this particular release installed, and can be used, for instance, to execute examples. For more details on the examples, see &lt;a href="https://ikarus-project.github.io/v0.4/02_examples/"&gt;here&lt;/a&gt;.
&lt;br /&gt;&lt;br /&gt;
This version's documentation (ikarus-docs-v0.4.zip) is also included, and it can be accessed by navigating to the top-level file "index.html" in a browser.
&lt;br /&gt;&lt;br /&gt;
The download instructions can be found &lt;a href="https://ikarus-project.github.io/v0.4/download/"&gt;here&lt;/a&gt;.
DOI: 10.18419/darus-3889/3
title: ikarus-v0.4.zip

description: Ikarus source code
DOI: 10.18419/darus-3891/1
title: figures_data_thermo_properties.tar.gz

description: Data for the four figures in the manuscript. This also includes the thermodynamic properties with different contributions.
DOI: 10.18419/darus-3891/2
title: Nb.tar.gz

description: The MTPs that make up the eMTP, along with the training set for each of the MTPs, the INCAR and KPOINTS used for generating the training data; the Hesse matrices used as reference in the thermodynamic integration; and the free energy contributions in parametrised form.
DOI: 10.18419/darus-3891/3
title: TaVCrW.tar.gz

description: The MTPs that make up the eMTP, along with the training set for each of the MTPs, the INCAR and KPOINTS used for generating the training data; the Hesse matrices used as reference in the thermodynamic integration; and the free energy contributions in parametrised form.
DOI: 10.18419/darus-4059/1
title: RADr_Arduino_Code.zip

description: Radr Arduino code
DOI: 10.18419/darus-4059/2
title: RADr.3dm

description: Radr and digital material 3D model
(in binary open source file format by openNURBS initiatve)
DOI: 10.18419/darus-4059
title: Roaming Autonomous Distributed robot (RADr)

description: Roaming Autonomous Distributed robot (RADr) is a two-wheeled mobile robot that can assemble hexagonal digital materials. This dataset contains the 3D models for a RADr and a digital material that the RADr can actively grab with an electromagnet and move around (in 3dm and STEP file format). On top of each, there are retroreflective markers so that can be tracked by a motion capture system such as Optitrack or Vicon. The dataset also contains the robot code that is uploaded to the robot to enable its wireless control via MQTT. MQTT is a standard messaging protocol for the Internet of Things (IoT). 
&lt;p&gt;
&lt;p&gt;
The electronic components on the robot are as follows:
&lt;ul&gt;
&lt;li&gt;Arduino Uno Wi-Fi Rev2 &lt;/li&gt;
&lt;li&gt;2 ROBOTIS DYNAMIXEL XL430-W250-T with Wheel Set for TurtleBot3&lt;/li&gt;
&lt;li&gt;ROBOTIS DYNAMIXEL Motor Shield&lt;/li&gt;
&lt;li&gt;11.1 V 2250 mAh Lipo Battery&lt;/li&gt;
&lt;li&gt;8kg Electromagnet &lt;/li&gt;
&lt;li&gt;MOSFET&lt;/li&gt;
&lt;/ul&gt;
description: Arduino IDE, 2.0.3
description: Rhinoceros 3D, 7.36
DOI: 10.18419/darus-4059/3
title: RADr.stp

description: Radr and digital material 3D model (in ASCII file format used for CAD data exchange)
DOI: 10.18419/darus-4068/1
title: Plot_hpd_vd_hdi.py

description: Executable Pyhton file for the HPD vs HDI analysis. The posterior of the clinical data is calculated with the BayModTS workflow: https://github.com/Systems-Theory-in-Systems-Biology/BayModTS
DOI: 10.18419/darus-4068/2
title: liver_resection_bayesian_inference.zip

description: Bayesian Inference Results of the clinical dataset using BayModTS. Results include the posterior, the PEtab problem for inference. Further the results of the HPDR vs. PI comparison are included.
DOI: 10.18419/darus-4068
title: Percentile Intervals in Bayesian Inference are Overconfident

description: &lt;p&gt;This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material).&lt;/p&gt;

The data includes Computed Tomography (CT) liver volume measurements of patients before (POD 0) and after partial hepatectomy. Liver volume was normalized per patient to the preoperative liver volume. was used to screen the liver regeneration courses. The Fujifilm Synapse3D software was used to calculate volume estimates from CT images. The data is structured in a tabular separated value file of the PEtab format.
DOI: 10.18419/darus-4068/3
title: results - broader prior validation.zip

description: Validation of Bayesian Inference results using a broader normal prior
DOI: 10.18419/darus-4122/1
title: DaRUS.zip
DOI: 10.18419/darus-4122/2
title: Buchmeiser_Mo.zip

description: Computational Data (Density Functional Theory)
DOI: 10.18419/darus-4122
title: Replication data of Buchmeiser group for: "Stereoselective Ring Expansion Metathesis Polymerization with Cationic Molybdenum Alkylidyne N-Heterocyclic Carbene Complexes"

description: Synthetic proofs in form of 1H, 13C, 19 F NMR spectra, as well as GPC traces for polymers, the presented MALDI-TOF spectrum, Data files for the crystal structure as well as data for the theoretical calculations are reported. All primary data files of measurements and processed data of the journal article mentioned under related publications from Buchmeiser and Kästner group can be found here. The data is structured according to figures and schemes in the research article and contains the following data types: bruker NMR folder, .cif, .txt., .pdf.
description: The ZIP file Buchmeiser Mo contains relevant calculation results which are the basis for the theoretical part of the paper. The .tgz file contains calculations on the charge distribution of complexes mentioned in the paper. The ZIP file Data contains the above mentioned data in order of Polymer types and is labeled as entries of tables.
DOI: 10.18419/darus-4122/3
title: K1.tgz
DOI: 10.18419/darus-691/1
title: projections_dry.rar

description: Projection images (360 deg, angle increment 0.2 deg), dark image (di) and open beam image (ob) in 16 bit *.tif file format of the dry sample.
DOI: 10.18419/darus-691/2
title: reconstructed_dry.rar

description: Reconstructed micro-XRCT data set in 16 bit *.tif file format of the dry sample. 2940x2940x2139 voxels with the uniform voxel size of 1.9996 µm.
DOI: 10.18419/darus-691/3
title: projections_saturated.rar

description: Projection images (360 deg, angle increment 0.2 deg), dark image (di) and open beam image (ob) in 16 bit *.tif file format of the saturated sample.
DOI: 10.18419/darus-691/4
title: reconstructed_saturated.rar

description: Reconstructed micro-XRCT data set in 16 bit *.tif file format of the saturated sample. 2940x2940x2139 voxels with the uniform voxel size of 1.9996 µm.
DOI: 10.18419/darus-1147/1
title: InnovativeSmartphoneLehre.apk

description: Android App (in binary APK format) that should run on any recent Android device)
DOI: 10.18419/darus-1147/2
title: license.txt

description: CC-BY-SA 4.0
DOI: 10.18419/darus-1147/3
title: Beispiel_Newtonverfahren.m4v

description: Example on using the app in pandemic teaching
DOI: 10.18419/darus-1147
title: Smartphone-Apps im Mathematikstudium

description: Bereitstellung einer Sammlung von Android-Apps zum Einsatz in der universitäten Mathematik-Ausbildung, gleichermaßen als Demonstratoren und zum Selbststudium.
DOI: 10.18419/darus-1147/4
title: install.txt
DOI: 10.18419/darus-1188/1
title: README.md
DOI: 10.18419/darus-1188/2
title: new_Class_Tumble_6.py
DOI: 10.18419/darus-1188/3
title: ver8_observables.py
DOI: 10.18419/darus-1188
title: Simulation Scripts for Motile Bacteria in a Channel

description: Simulation scripts to simulate bacteria in a channel. Motility patterns run and tumble or straight swimming are supported. Requires Espresso simulation package. (http://espressomd.org/) For more information, see README.MD
DOI: 10.18419/darus-1188/4
title: ver8_observables_no_walls.py
DOI: 10.18419/darus-1214/1
title: 00004011.JPG
DOI: 10.18419/darus-1214/2
title: 00004010.JPG
DOI: 10.18419/darus-1214/3
title: 00004009.JPG
DOI: 10.18419/darus-1214
title: 2D pictures of SO60/2-20

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: miscellaneous
DOI: 10.18419/darus-1214/4
title: 00004001.JPG
DOI: 10.18419/darus-1246/1
title: MEP-Pictures.tgz

description: Molecular Electrostatic Potential Maps as Pictures
DOI: 10.18419/darus-1246/2
title: Optimized-Structures.tgz

description: XYZ-files for the Optimized Structures before and after protonation, sorted according to the used functionals
DOI: 10.18419/darus-1246/3
title: BLYP-Partialcharges.tgz

description: Outputfiles containing information about the partial charges used for analysis
DOI: 10.18419/darus-1246
title: Publication data for: "Proton Affinities of N-Heterocyclic Olefins and Their Implications for Organocatalyst Design", data from Kästner group

description: All primary data files and processed data of the journal article from Kästner group. The input and output of each DFT calculation in one directory.
DOI: 10.18419/darus-1246/4
title: Structure-pictures-publication.tgz

description: Pictures of the investigated structures that were utilized in the publication.
DOI: 10.18419/darus-4254/1
title: Documentation_VR-Omics.pdf

description: Download instructions and documentation of VR-OMICS.
DOI: 10.18419/darus-4254/2
title: Test_Data_VR-Omics.zip

description: Test data generated for use with VR-Omics for different spatial transcriptomics sequencing techniqes.
DOI: 10.18419/darus-4254/3
title: VR_Omics.zip

description: Software VR-Omics (containing Python AW and Visualiser)
DOI: 10.18419/darus-4341/1
title: hnc_clean_strict_val.json

description: HNC validation set, automatically generated.
DOI: 10.18419/darus-4341/2
title: test_set_v1-0.json

description: HNC test set, annotated by humans.
DOI: 10.18419/darus-4341
title: Data for: HNC: Leveraging Hard Negative Captions towards Models with Fine-Grained Visual-Linguistic Comprehension Capabilities

description: Image-Text-Matching (ITM) is one of the defacto methods of learning generalized representations from a large corpus in Vision and Language (VL). However, due to the weak association between the web-collected image–text pairs, models fail to show fine-grained understanding of the combined semantics of these modalities. To this end, we propose Hard Negative Captions (HNC): an automatically created dataset containing foiled hard negative captions for ITM training towards achieving fine-grained cross-modal comprehension in VL. Additionally, we provide a challenging manually-created test set for benchmarking models on a fine-grained cross-modal mismatch with varying levels of compositional complexity. Our results show the effectiveness of training on HNC by improving the models’ zero-shot capabilities in detecting mismatches on diagnostic tasks and performing robustly under noisy visual input scenarios. Also, we demonstrate that HNC models yield a comparable or better initialization for fine-tuning.

The dataset consists of image-caption pairs stored in the JSON data format. The captions describe fine-grained aspects of the corresponding images and each positive caption is associated with exactly one hard negative caption.
DOI: 10.18419/darus-4341/3
title: hnc_clean_strict_train.json

description: HNC training set, automatically generated.
DOI: 10.18419/darus-4359/1
title: 01_SurfaceBasedSyntaxApproach.gh
DOI: 10.18419/darus-4359/2
title: 02_InterlacedSyntaxApproach.gh
DOI: 10.18419/darus-4359
title: Fiber Pattern Generation Tools for the Design of Long-Span, Core-Less Wound, Structural Composite Building Elements

description: This data set contains tools to generate fiber patterns for tubular surface components as used in the BUGA Fibre Pavilion (&lt;a href="https://www.intcdc.uni-stuttgart.de/research/building-demonstrators/bd-2/"&gt;Website&lt;/a&gt;). The tools allowed to model the fiber patterns as polylines and anchor point sequences which acted as a base for planning the robotic motion paths for prefabrication of the BUGA Fibre composite components.&lt;br&gt;
The surface-based syntax tool generates local support surfaces to keep subsequent fibers in place. The resulting fiber pattern is fully parametric and can be tailored to the specific requirements of different fiber components. 
The interlaced syntax tool generates a smooth fiber body by continuously revolving around the component's long axis and alternating between boundary frames. The output is a continuous polyline. In the second step, the resulting fiber net is relaxed to approximate the final anticlastic shape of the fiber lattice. &lt;br&gt;
The reinforcement tool allows the modeling of carbon fiber reinforcement patterns. Following a similar logic as the surface-based tool, the reinforcement wraps around the component and is then relaxed to approximate the final shape of the composite.
description: Rhinoceros, 7
description: Grasshopper, 1.0.0007
description: Kangaroo Physics Engine, 0.096
DOI: 10.18419/darus-4359/3
title: 03_Reinforcement.gh
DOI: 10.18419/darus-4443/2
title: IntCDC-Building_-_Ecological_Quality_-_Final_Report.pdf
DOI: 10.18419/darus-4491/1
title: DP-FR-1924-MODLWT_Re15E5.trns

description: Transition location
DOI: 10.18419/darus-4491/2
title: DP-FR-1924-MODLWT_Re15E5.POL

description: Polar measurements
DOI: 10.18419/darus-4491
title: DP-FR-1924-MODLTW Polar Data Re15E5

description: Polar measurements of the DP-FR-1924-MODLWT airfoil. Data recorded at the Laminar Wind Tunnel, University of Stuttgart. For details on the measurements, visit &lt;a href="https://www.iag.uni-stuttgart.de/en/institute/test-facilities/laminar-wind-tunnel/"&gt;https://www.iag.uni-stuttgart.de/en/institute/test-facilities/laminar-wind-tunnel/&lt;/a&gt;
DOI: 10.18419/darus-4491/3
title: POLkey.png
DOI: 10.18419/darus-1671/1
title: interface_preserving_moving_mesh.tar.xz
DOI: 10.18419/darus-1671/2
title: README.md
DOI: 10.18419/darus-1671/3
title: interface_preserving_moving_mesh.tar.xz
DOI: 10.18419/darus-1671/4
title: README.md
DOI: 10.18419/darus-1749/1
title: DSC09063.ARW
DOI: 10.15468/dl.57xgpy
title: Occurrence Download

description: A dataset containing 2804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aves)",
 "HasCoordinate is one of (true)",
 "Geometry POLYGON((-62.7239351890919 -13.6086714957021,-61.7812372315987 -13.6086714957021,-61.7812372315987 -12.9642222768067,-62.7239351890919 -12.9642222768067,-62.7239351890919 -13.6086714957021))"
 ]
}

 The dataset includes 2804 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003391-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18419/darus-1749/2
title: DSC09063.JPG
DOI: 10.18419/darus-1749/3
title: DSC09064.JPG
DOI: 10.18419/darus-1749
title: 2D pictures of MÜ30-18

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: accessories
DOI: 10.18419/darus-1749/4
title: DSC09064.ARW
DOI: 10.18419/darus-1762/1
title: DSC09205.ARW
DOI: 10.18419/darus-1762/2
title: DSC09206.ARW
DOI: 10.18419/darus-1762/3
title: DSC09206.JPG
DOI: 10.18419/darus-1762
title: 2D pictures of MÜ78-18

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: accessories for demonstrations
DOI: 10.18419/darus-1762/4
title: DSC09205.JPG
DOI: 10.18419/darus-1772/1
title: Kristalldaten.zip
DOI: 10.18419/darus-1772/2
title: GC-MS Daten.zip
DOI: 10.18419/darus-1772/3
title: NMR Daten.zip