cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-2176
title: Replication data of Buchmeiser group for: "Cationic Tungsten Imido Alkylidene N-Heterocyclic Carbene Complexes That Contain Bulky Ligands"

description: Neutral and cationic tungsten imido alkylidene complexes of the general formulas W(NtBu)(CHR1)(OR2)Cl(NHC),W(N-2,6-bis(2,4,6-tri-iPr-C6H4)phenyl)(CHR1)Cl2(NHC), [W(NtBu)(CHR1)(OR2)(NHC)][B(ArF)4] and [W(N-2,6-bis(2,4,6-tri-iPr-C6H4)phenyl)(CHR1)Cl(NHC)][B(ArF)4] (R 1 = CMe3, CMe2Ph; R2 = sterically demanding alkoxide; B(ArF)4 =tetrakis(3,5-(CF3)2-C6H3)borate; NHC = N-heterocyclic carbene) were prepared. Two electronically different NHCs, namely 1,3-dimethylimidazol-2-ylidene (IMe) and 1,3-dimethyl-4,5-dichloroimidazol-2-ylidene (IMeCl), as well as a variety of terphenolates and a chiral biphenolate were employed. Z-selective homometathesis (HM) of unfunctionalized olefins was achieved with a selectivity of up to 90% and decent turnover numbers (TON) of up to 480 in the HM of 1-dodecene. Additionally, the reactivity of the cationic tungsten tert-butylimido complexes in the reaction with vinyltrimethylsilane and ethylene was investigated, which yielded the corresponding silyl-alkylidene complex and, for the first time, a fully characterized cationic tungsten(IV) NHC ethylene complex.

All primary data files of measurements and processed data of the journal article mentioned under related publications from Buchmeiser group can be found here. 
The data is structured according to figures and schemes in the research article and contains the following data types: NMR Spectra
DOI: 10.18419/darus-2176/1
title: NMRs.zip
DOI: 10.18419/darus-2228
title: Replication data of C4 group for: "Interplay of Polarity and Confinement in Asymmetric Catalysis with Chiral Rh Diene Complexes in Microemulsions"

description: In this dataset, all simulation data are listed. That includes all geometry optimizations, single-point calculations, and IBO calculations. Furthermore, thermal corrections for the calculation of the equilibrium constants are listed. Additionally, the spreadsheet with the collected data and the figures with the visualizations of the IBOs are listed.
description: The simulation dataset includes all geometry optimizations including thermal corrections, single-point energy calculations, IBO calculations, and IBO visualizations. It is organized as follows: The file includes separate folders sorted by calculation type.
DOI: 10.18419/darus-2228/1
title: Simulation_data.tgz

description: Simulation dataset including all geometry optimizations with thermal corrections, single-point energy calculations, IBO calculations, and IBO visualizations and spreadsheet. Files are in separate folders sorted by calculation type. Each calculation type folder 
 includes a subfolder for each complex. The naming of the complexes is similar as in the publication.
DOI: 10.18419/darus-2251
title: 3D CT-Data for KK17/6-09

description: Research data (mostly *.hdf5 format) which was obtained by scanning the object via Computed Tomography and generating a 3D inside view of the object with non-invasive means.&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: directional gyro
DOI: 10.18419/darus-2251/1
title: KK17-06-09.hdf5

description: 3D Voxelized volume
DOI: 10.18419/darus-2810
title: Sample Implementation for the Paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection"

description: Java source code of proof-of-concept tool used for the paper "Eye Gaze on Scatterplot: Concept and First Results of Recommendations for Exploration of SPLOMs Using Implicit Data Selection".&lt;br&gt;
Code by M.Sc. student &lt;a href="mailto:yan@student.tugraz.at"&gt;Jia Jun Yan&lt;/a&gt;. Supervision and concepts by &lt;a href="mailto:nils.rodrigues@visus.uni-stuttgart.de"&gt;Nils Rodrigues&lt;/a&gt;, &lt;a href="mailto:l.shao@cgv.tugraz.at"&gt;Lin Shao&lt;/a&gt;, &lt;a href="mailto:tobias.schreck@cgv.tugraz.at"&gt;Tobias Schreck&lt;/a&gt;, and &lt;a href="mailto:weiskopf@visus.uni-stuttgart.de"&gt;Daniel Weiskopf&lt;/a&gt;.
description: Written to support The Eye Tribe Tracker.

Setup JavaFX on IntelliJ: https://openjfx.io/openjfx-docs/#install-javafx

Important: In order to run JavaFX on IntelliJ, you need to add the following line in Debug Options (Run &gt; Edit Configuration...)<br>
<pre>--module-path ".\lib\openjfx-sdk-16.0.1\lib" --add-modules javafx.controls,javafx.fxml</pre>
DOI: 10.18419/darus-2810/1
title: tool.zip
DOI: 10.18419/darus-2819
title: WENO Library for OpenFOAM

description: Weighted essentially non-oscillatory library for the framework of OpenFOAM.
Detailed information can also be found here: &lt;a href="https://github.com/WENO-OF/WENOEXT"&gt;https://github.com/WENO-OF/WENOEXT&lt;/a&gt;
DOI: 10.18419/darus-2819/1
title: WENOEXT.zip
DOI: 10.18419/darus-2828
title: DG Data Sets of Bubble Growth in Flashing Jets

description: &lt;p&gt;Initial Discontinuous Galerkin (DG) data sets of flashing bubble growth of a jet slice and jet tip configuration. Cases and results can also be found in:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;D. Dietzel, "Modeling and simulation of flash-boiling of cryogenic liquids", PhD Thesis, University of Stuttgart, 2020. &lt;a href="https://doi.org/10.18419/opus-10974"&gt;doi: 10.18419/opus-10974&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;D. Dietzel, T. Hitz, C.-D. Munz, and A. Kronenburg, “Numerical simulation of the growth and interaction of vapour bubbles in superheated liquid jets,” International Journal of Multiphase Flow, vol. 121 (2019), article 103112.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;The solver FS3D.comp (FLEXI) is provided with this data set. Relevant git commits used to obtain the published results can be looked up in the PhD thesis, Appendix G (&lt;a href="https://doi.org/10.18419/opus-10974"&gt;doi: 10.18419/opus-10974&lt;/a&gt;).&lt;/p&gt;

The data set is build up as follows:
&lt;dl&gt;
&lt;dt&gt;bubble_column&lt;/dt&gt;
&lt;dd&gt;Investigated test cases of a bubble column for a superheat ratio of 5, an ambient temperature of 120 Kelvin and an R0 Star of 50. The cases also contain postprocessing scripts in octave. &lt;/dd&gt;
&lt;dt&gt;ILASS17Flexi&lt;/dt&gt;
&lt;dd&gt;Git versioned source code of the simulation tool FS3D.comp (FLEXI). Versions used for publications can be looked up up in the PhD thesis, Appendix G (&lt;a href="https://doi.org/10.18419/opus-10974"&gt;doi: 10.18419/opus-10974&lt;/a&gt;) &lt;/dd&gt;
&lt;dt&gt;jet_slice&lt;/dt&gt;
&lt;dd&gt;Same conditions as for the bubble column, but now the test case consists of a jet slice with only one cyclic boundary condition.&lt;/dd&gt;
&lt;dt&gt;jet_tip&lt;/dt&gt;
&lt;dd&gt;Same conditions as for the bubble column, but now the test case consists of a jet tip to have three open directions for expansion of the jet.&lt;/dd&gt;
&lt;dt&gt;postpro&lt;/dt&gt;
&lt;dd&gt;Postprocessing scripts with octave&lt;/dd&gt;
&lt;/dl&gt;
description: FS3D.comp (FLEXI), null
DOI: 10.18419/darus-2828/1
title: DG-Data.tar.gz
DOI: 10.18419/darus-2842/1
title: fairsaxs_220512.zip
DOI: 10.18419/darus-2843
title: Guidelines on Replication and Research Data Management

description: This document summarises guidelines for reproducibility in SFB/Transregio 161 by Task Force A (TF-A). It builds upon the data management plan (version
1.0, 2020, &lt;a href="https://doi.org/10.18419/darus-632"&gt;https://doi.org/10.18419/darus-632&lt;/a&gt;) and focusses on three main points: 

&lt;ol&gt;
&lt;li&gt;Provide clear definitions of reproducibility and replicability in the context of TF-A.&lt;/li&gt;
&lt;li&gt;Coordinate efforts on research data preservation along the research pipeline.&lt;/li&gt;
&lt;li&gt;Provide guidelines on how to achieve reproducibility and facilitate replicability using DaRUS, how to deal with further scenarios, and criteria for comparable third-party repositories.&lt;/li&gt;
&lt;/ol&gt;
DOI: 10.18419/darus-2843/1
title: TFA-Guidelines-2022.pdf
DOI: 10.18419/darus-2886
title: Handwriting Inpainting Dataset

description: &lt;p&gt;The dataset contains binary handwriting masks, which are sampled from scanned pages. Based on the overlay size, the training and test datasets are divided into five size ranges: 0-5%, 5-10%, 10-15%, 15-20% and 20-25% of the image.&lt;/p&gt;
DOI: 10.18419/darus-3055
title: Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution

description: &lt;p&gt;Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution".&lt;/p&gt;

&lt;p&gt;
Contains:
&lt;ul&gt;
&lt;li&gt;math behind Relaxed Dot Plots&lt;/li&gt;
&lt;li&gt;additional images&lt;/li&gt;
&lt;li&gt;pseudo-anonymous study data&lt;/li&gt;
&lt;li&gt;source code for library and test application&lt;/li&gt;
&lt;/ul&gt;
&lt;/p&gt;

&lt;p&gt;
To view the material, extract &lt;code&gt;supplemental.zip&lt;/code&gt; and open &lt;code&gt;index.html&lt;/code&gt; in a web browser.
&lt;/p&gt;
description: Math jointly by Nils Rodrigues and Tim Krake. Source code mostly by Sören Döring and Daniel Baumgartner with some details by Nils Rodrigues. Concept jointly by Nils Rodrigues, Christoph Schulz, Sören Döring, Daniel Baumgartner, Tim Krake, and Daniel Weiskopf.
DOI: 10.18419/darus-3055/1
title: supplemental.zip
DOI: 10.18419/darus-2488
title: Efficient three-material PLIC Implementation as Fortran Module

description: The dataset provides a Fortran module with the code for the efficient three-phase positioning method.
DOI: 10.18419/darus-2488/1
title: plicmod.F

description: Fortran module, implementation of sequential efficient three-material PLIC positioning
DOI: 10.18419/darus-2493/1
title: Informations about GO06-01.pdf
DOI: 10.18419/darus-2495/1
title: Informations about GO06-02.pdf
DOI: 10.18419/darus-2497/1
title: Information about GO07-01.pdf
DOI: 10.18419/darus-2499/1
title: Information about GO07-02.pdf
DOI: 10.18419/darus-2501/1
title: Information about GO07-03.pdf
DOI: 10.18419/darus-2503/1
title: GO07-04.pdf
DOI: 10.18419/darus-2505/1
title: GO07-05.pdf
DOI: 10.18419/darus-2468
title: Synthesis and Hydrolysis of Cephalexin

description: Investigated was the synthesis and hydrolysis of cephalexin, and the hydrolysis of phenylglycine methyl ester by an amino ester hydrolase. The concentrations of all reactants over time were measured, for four different initial concentrations of cephalexin and phenylglycine methyl ester. For each of the reactants, 9 data points are given over a time of 30 minutes.
description: PyEnzyme, 1.1.0
description: This dataset was created using the EnzymeML spreadsheet template and PyEnzyme conversion. In order to investigate the dataset, it is advised to use the PyEnzyme library.
DOI: 10.18419/darus-2468/1
title: EnzymeML_Lagerman.omex
DOI: 10.18419/darus-2609
title: Python implementation of heat accumulation equations for additive manufacturing

description: This is a python implementation of the heat accumulation equations, which describe the increase of the residual temperature in additive manufacturing. The Derivation of the equations is described in the related paper given below.
If you run the script in the console, it creates two text-files and four plots in .png format.
Process parameter and material characteristics can be changed or adapted in the beginning of the script.
Detailled instructions are given within the code by the comments.
description: Python, 3.9.13
DOI: 10.18419/darus-2609/1
title: heat_accumulation_AM.py
DOI: 10.18419/darus-3280
title: Cross-chain Smart Contract Invocations - a Multi-vocal Literature Review Protocol

description: This document introduces the protocol that describes how a Multivocal Literature Review (MLR) regarding Cross-chain Smart Contract Invocation (CCSCI) was planned to be conducted. The main purpose of having this protocol is to avoid bias when selecting primary sources and processing them.
Furthermore, it helped co-authors gain an overview of the topic and refine the planned research methods before the actual MLR started.
DOI: 10.18419/darus-3280/1
title: Protocol.pdf

description: The protocol document.
DOI: 10.18419/darus-3281
title: Qualitative Visual Analysis of Cross-chain Smart Contract Invocation Approaches

description: This document demonstrates the visual qualitative analysis performed over the approaches systematically selected for inclusion in the study entitled, "Cross-Chain Smart Contract Invocations: A Systematic Multi-Vocal Literature Review".
DOI: 10.18419/darus-3281/1
title: Visual Analysis.pdf

description: Qualitative visual analysis of the selected CCSCI approaches.
DOI: 10.18419/darus-3291/1
title: codemeta.tsv
DOI: 10.18419/darus-3310
title: Replication Data of Kästner group for: "Determination of Accessibility and Spatial Distribution of Chiral Rh Diene Complexes Immobilized on SBA-15 via Phosphine‑based NMR Probe Molecules"

description: In this dataset, all simulation data are listed. That includes all geometry optimizations and single-point calculations and the conformational sampling. Furthermore, the spreadsheet with the collected data is listed. The folders are named similar to the nomenclature in the publication. All calculations were performed in Turbomole via Chemshell and the collected data is processed in the spreadsheet. The optimized geometries are named geom_opt.xyz and the resulting conformers from the conformational sampling can be found in the file crest_conformers.xyz. The single-point energies can be found in the out-files.
DOI: 10.18419/darus-3310/1
title: calculations.tgz

description: In this dataset, all simulation data are listed. That includes all geometry optimizations and single-point calculations and the conformational sampling data. Furthermore, the spreadsheet with the collected data is listed. The folders are named similar to the structures in the publication. Each folder contains all calculations belonging to the structure it is named after. The spreadsheet contains all processed results.
DOI: 10.18419/darus-3332
title: Data for Fault Diagnosis in Adaptive Buildings

description: &lt;p&gt;
&lt;b&gt;General information:&lt;/b&gt;&lt;br&gt;
This dataset is meant to serve as a benchmark problem for fault detection and isolation in dynamical systems. It contains pre-processed sensor data from the adaptive high-rise demonstrator building D1244, built in the scope of the &lt;a href="https://www.sfb1244.uni-stuttgart.de/en/"&gt;CRC1244&lt;/a&gt;. Parts of the measurements have been artificially corrupted and labeled accordingly. Please note that although the measurements are stored in Matlab's .mat-format (Version 7.0), they can easily be processed using free software such as the SciPy library in Python.
&lt;/p&gt;

&lt;p&gt;
&lt;b&gt;Structure of the dataset:&lt;/b&gt;&lt;br&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;b&gt;train&lt;/b&gt; contains the training data (only nominal)&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;test_easy&lt;/b&gt; contains test data (nominal and faulty with high fault amplitude). Faulty samples were obtained by manipulating a single signal in a random nominal sample from the test data.&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;test_hard&lt;/b&gt; contains test data (nominal and faulty with low fault amplitude)&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;meta&lt;/b&gt; contains textual labels for all signals and fault types&lt;/li&gt;
&lt;/ul&gt; 
&lt;/p&gt;

&lt;p&gt;
&lt;b&gt;File contents:&lt;/b&gt;&lt;br&gt;
Each file contains the following data from 16384 timesteps:
&lt;ul&gt;
 &lt;li&gt;&lt;b&gt;t:&lt;/b&gt; time in seconds&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;u:&lt;/b&gt; demanded actuator forces in newtons&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;y:&lt;/b&gt; measured outputs (relative elongations measured by strain gauges and actuator displacements in meters measured by position encoders)&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;label:&lt;/b&gt; categorical label of the present fault class, where 0 denotes the nominal class and faults in the different signals are encoded according to their index in the list of fault types &lt;code&gt;meta/labels.txt&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;
Faulty samples additionally include the corresponding nominal values for reference
&lt;ul&gt;
 &lt;li&gt;&lt;b&gt;u_true:&lt;/b&gt; delivered actuator forces&lt;/li&gt;
 &lt;li&gt;&lt;b&gt;y_true:&lt;/b&gt; measured outputs without faults&lt;/li&gt;
&lt;/ul&gt;
A sample's textual fault label is also contained in its filename (between the first and second underscore).
&lt;/p&gt;
DOI: 10.18419/darus-3332/1
title: adaptive_building_diagnosis_data.zip
DOI: 10.18419/darus-3383
title: Replication Data for: Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments

description: The file contains a Unity project, which allows to test the desktop-based visualization techniques introduced in the Paper "Been there, Seen that: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments". It allows you to analyze 3D gaze and movement data sets recorded using the HoloLens 2. It provides you with a gaze replay visualization, which is linked to a space-time cube visualization to show an overview of the behavioral data and inspect important events in more detail.&lt;br&gt;
The project includes a folder called Assets, which contains the necessary scripts and data. The project can be opened using Unity. We recommend Unity version 2020.3.24f. The scenes GazeReplay and STC need to be dragged into the hierarchy window and the GazeReplay scene must be unloaded. Afterward, the visualization can be viewed and tested within the game view by hitting the play button.
&lt;pre&gt;
&lt;Assets&gt;
.
└── MyScripts
 └── General
 |── ButtonFunctionalities # the code for UI elements
 |── ReadData # the code for loading the data
 |── Trajectory # visualizes movement within space-time cube(STC) 
 |── StackedHeatMap # visualizes cube heatmap within STC
 |── HeatmapWall # visualizes heatmap within gaze replay 
 |── ReplayManager_General # visualizes participants within gaze replay 
└── Resources
 └── CSVFiles
 └──AnchorFile #contains the files needed to transform the data into one 
 coordinate system
 └──GazeData #contains the recorded gaze data of the participants
└── Scenes
 |── GazeReplay # Scene for gaze replay 
 |── STC # Scene for STC 
&lt;/Assets&gt;
&lt;/pre&gt;
Please check the &lt;a href="https://github.com/IntCDC/BTST.git"&gt;GitHub page&lt;/a&gt; for the latest version.
DOI: 10.18419/darus-3383/1
title: BTST-main.zip

description: This file contains the Unity project for the desktop-based visualization techniques introduced in the paper "Been There, Seen That (BTST): Visualization of 3D eye tracking data from real world environments"
DOI: 10.18419/darus-3120
title: Viscosities of experimental aqueous methanol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3120/1
title: exp_visc_meth.xml
DOI: 10.18419/darus-3121
title: Viscosities of experimental aqueous glycerol mixtures

description: In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol-water and glycerol-water, by systematically studying the dependence of densities and diffusion coefficients from water content over the whole composition range and temperatures between 278.15 and 318.15 K. Experimental data was extracted manually from literature. The same parameter space was explored by comprehensive molecular dynamics simulations, whose results were directly transferred to the analysis platform. The benefit of data integration was illustrated by assessing the transferability of the force fields, which had been developed for pure compounds to different compositions and temperatures, and by analyzing the excess mixing properties as a measure of non-ideality of methanol-water and glycerol-water mixtures. The core of the data management and analysis platform is the newly developed Python library pyThermoML, which represents metadata, the parameters and the experimentally determined or simulated properties as Python data classes. 
&lt;br&gt;&lt;/br&gt;
The feasibility of a seamless data flow from data acquisition to a comprehensive data analysis was demonstrated. &lt;a href="https://github.com/FAIRChemistry/pyThermoML"&gt;PyThermoML&lt;/a&gt; enables interoperability and reusability of the datasets. The publication of ThermoML documents on the Dataverse installation of the University of Stuttgart (DaRUS) makes thermophysical data findable and accessible, and thus FAIR.
&lt;br&gt;&lt;/br&gt;
The usage of pyThermoML is demonstrated in the following &lt;a href="https://github.com/FAIRChemistry/pyThermoML/blob/master/pyThermoML_example_workflow/templateThermoML.ipynb"&gt;example workflow&lt;/a&gt; and can be utilized to read the given ThermoML file.
DOI: 10.18419/darus-3121/1
title: exp_visc_glyc.xml
DOI: 10.15468/dl.k9kdjd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dendrocopos major (Linnaeus, 1758)",
 "Year is 33",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003328-241107131044228/datasets/export for details.