Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Extended data integration of motivational aspects in gamification and game-based learning educational designs

This is the extended data for a systematic review article about the integration of motivational aspects in gamification and game-based learning educational designs related to teacher´s training and teacher´s professional development.

opencc-by-4.0Aug 2024View details →

Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data

<h1>Neural network potentials of an excess proton in bulk water, training data</h1> This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3). The configurations are given as a single XYZ file: configurations.xyz The box dimensions are written in box.txt The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line) The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz

Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"

This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper: Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390 Abstract: Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.

Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"

Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831 There are 2 files in this upload: 1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon. 2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".

Data from: Repairing a deleterious domestication variant in a floral regulator of tomato by base editing

This repository contains the data necessary to run the analysis described in the publication "Repairing a deleterious domestication variant in a floral regulator of tomato by base editing" by Glaus et al., 2024. A preprint is available on bioRxiv (doi: <a href="https://doi.org/10.1101/2024.01.29.577624" rel="nofollow">https://doi.org/10.1101/2024.01.29.577624</a>)   82_acc_Spim0.1_filtered.vcf.gz -- variant call results for 82 genomes with LA1589 as reference 82_acc_Spim0.1_filtered_SIFT_out.tar.gz -- sift4g prediction results for 82 genomes with LA1589 as reference (SIFTannotations.xls and SIFTpredictions.list) sift_lib_LA1589.tar.gz -- sift4g library for the LA1589 genome SolpimLA1589_liftoff.tar.gz -- liftoff annotation of LA1589 genome   In case of any questions, please contact Sebastian Soyk (sebastian.soyk@unil.ch)

Training data set for: Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries

<h2>Overview</h2> This is a synthetic volcano deformation dataset accompanying the publication of Graph Neural Network based elastic deformation emulators for magmatic reservoirs of complex geometries, on the journal Volcanica. Synthetic, quasi-static deformation is computed for magma chambers of various geometries, parameterized as spheroids or superpositions of spherical harmonics. Surface deformation is computed using the boundary element method (BEM) of Nikkhoo & Walter (2015). Please reference our paper for details of computational methods. The dataset contains 50,000 realizations of magma chamber geometries/orientations/centroid depths and associated deformation fields. Surface deformation fields are sampled at discrete locations, with a uniform random distribution within [Lh x Lh], and a distribution that concentrates near the chamber (at radial distances, r = 10^(-3  random number) * Lh/2). Note this dataset contains only a small fraction of the total dataset. In total, 824,393 realizations of magma chambers were used to train our emulators. For accessing the complete training data set, please contact the authors.  Each .mat file contains the deformation field associated with a single chamber geometry. Use visData.m to visualize chamber geometry and associated surface displacement. Each file contains two MATLAB structures, "input" and "output".  <h2>Naming of each zip file</h2> The numbers after the underscore, N:M, indicate that this file contains N of the M total chamber realizations for this particular setup.  <a href="../api/records/13800065/draft/files/sph_20AspRatios_1e4:151211.zip.zip/content" target="_blank" rel="noopener noreferrer">sph_20AspRatios_1e4:151211.zip</a>: deformation corresponding to spheroidal magma chambers parameterized by aspect ratios.  <a href="../api/records/13800065/draft/files/sh_complex_1e4:152283.zip/content" target="_blank" rel="noopener noreferrer">sh_complex_1e4:152283.zip</a>: deformation corresponding to chamber geometry produced by superposition of spherical harmonic modes.  <a href="../api/records/13800065/draft/files/sh_mode_approx_1e4:138380.zip/content" target="_blank" rel="noopener noreferrer">sh_mode_approx_1e4:138380.zip</a>: deformation corresponding to chamber geometries corresponding to individual spherical harmonic modes, combined with a spherical mode (the spherical mode prevents chamber surfaces from having zero radii locally) <a href="../api/records/13800065/draft/files/sh_spheroid_approx1e4:202272.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_approx1e4:202272.zip</a>: deformation corresponding to chambers approximating spheroids, but parameterized by spherical harmonics. <a href="../api/records/13800065/draft/files/sh_spheroid_perturb_1e4:180247.zip/content" target="_blank" rel="noopener noreferrer">sh_spheroid_perturb_1e4:180247.zip</a>: same as above, but with additional random perturbations parameterized in spherical harmonics. <h2>Variables in each file</h2> Input contains the following fields: dp2mu: pressure change to shear modulus ratio. dx, dy, dz: the coordinates of chamber centroid [meters] mu: dimensionless crustal shear modulus (always set to 1) nu: crustal Poisson's ratio (always set to 0.25) Ns: number of points on the surface where displacements are computed Lh, Lv: horizontal and vertical dimensions of the model domain [meters]. Lh is determined such that at the edge of the model domain, the displacement magnitude is below 10 percent of the maximum. Lv = Lh/2 + abs(dz) for the spheroids ----------------------------------------------------------------------------------------------------------- the input files contain asp: aspect ratio of chamber (length of the semi-major axis divided by that of the semi-minor axis) ra, rb: semi-major, -minor, axis length [meters] thetax, thetay, thetaz: counterclockwise rotation angles with regard to x, y, z axis [degrees]. thetax = [0, 90] degrees, thetay = 0 degrees, thetaz = 360 degrees. for the general geometries-------------------------------------------------------------------------------------------------- the input files contain ls, ms, fs: degree, order, coefficients of spherical harmonic modes. Spherical harmonics are sampled up to degree 5. fs is a complex vector of coefficients such that the resulting shape is real.  normF: normalization factor applied to the shape parameterized by ls, ms, fs, such that the shape as a maximum radius of unity. rmax: scale factor to scale the spherical harmonics parameterized shape to real dimensions [meters]. ============================================================================================= Output contains the following fields, X, Y, Z: coordinates of points where displacement vectors are computed [meters] Ux, Uy, Uz: displacements in x, y, z directions [meters] P, T: coordinates [meters] of vertices for the triangular mesh used in BEM calculation, and the connectivity matrix  C: coordinates [meters] of the center of each triangular element that, dhat, nhat: unit vectors for orthogonal coordinate systems local to each triangular element. that ("t-hat") extends from vertex one to vertex two, nhat is outward normal, and dhat = cross (nhat, that). Reference: 1. Nikkhoo, M., & Walter, T. R. (2015). Triangular dislocation: an analytical, artefact-free solution. Geophysical Journal International, 201(2), 1119-1141.

Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning

The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571.  <h2>Experimental Description:</h2> The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6). <h3>Pulse Testing:</h3> The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V. Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses. <h3>Driving Cycle Testing:</h3> The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries. As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations. <h2>File Naming Conventions:</h2> The dataset files are named based on the experimental conditions, as follows: <ul> <li>Pulse_data_tempX_batY: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li>Driving_cycle_data_tempX_batY: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>

Fluid-rock interaction of metamorphic fluids caused base metal mineralization in the Moldanubian domain, Bohemian Massif, Czech Republic – a fluid inclusion study - Supplementary data + measuring conditions

Open the record for dataset details and reuse information.

Compilation of data collected in surveys on the WissKI-based 3D Repository with DFG 3D-Viewer project partners and architecture students from the Warsaw University of Technology and the Technical University of Łódź

The dataset contain the compiliation of responses from users of WissKI-based 3D Repository (https://3d-repository.hs-mainz.de/)., which is the open platform for deposit of 3D models of cultural heritage. The beta version of the WissKI 3D Repository, initiated in June 2022, has been subjected to evaluation by two primary target groups since its launch. The initial group, composed of students in the cultural heritage domain, was tasked with showcasing the importance of documenting and publishing 3D models of digital reconstructions. The survey with sutdents was conducted for three different classes:  1) In summer 2022 with bachelor architectrue students at Warsaw University of Technology during seminar of choice regarding digital reconstruction of wooden synagogues; 2) In summer 2023 with bachelor architectrue students at Warsaw University of Technology, and master students from Technology University of Łódź during seminar of choice regarding digital reconstruction of wooden synagogues; 3) In autumn 2023 during international workshop about digital 3D heritage of CoVHer project with studnets of architecture from Warsaw Univeristy of Technology, Alma Mater Studiorum – Universita di Bologna, Facoltà di Architettura di Porto and Hochschule Mainz - University of Applied Sciences, as well as archaeology studnets from Universitat Autònoma de Barcelona. The second group, comprising digital 3D cultural heritage professionals, predominantly focused on archiving digital assets. Participants were project partners of DFG 3D Viewer project, which were professionals from the Institute of Archaeology at University Cologne, the Institute of Art History at the Ludwig-Maximilians-Universität Munich, the Architecture, Civil Engineering and Urban Planning Department of BTU Cottbus Senftenberg, and the Detushce Museum. They were asked for evaluaton of system after three differetn stages of work: at the begging wihtout any introduction to the system, after proivision of intorudctionary materilas and finally at the end of work. All participants were requested to report their experiences across four categories: metadata form, 3D viewer, provided guidelines, and overall experience. A 5-point rating scale was employed to assess specific issues, with 1 being the most negative and 5 being the most positive. The form length question was an exception, where a median value of 3 was considered ideal, and extreme values indicated either excessive length or brevity.

Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology

This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern. The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.

Characterization Data for the Manuscript: "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments"

This entry contains characterization data for the manuscript "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments".

opencc-by-4.0Oct 2024View details →

Data for "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model"

Data to accompany: O’Neill, J.F., Edwards, T.L., Martin, D.F., Shafer, C., Cornford, S.L., Seroussi, H.L., Nowicki, S., Adhikari, M., Gregoire, L.J.. (2024). "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere". The Cryosphere. DOI: 10.5194/egusphere-2024-441 (preprint) Zipped directories called ismip6_expname_8km containing NetCDFs of output data from each experiment, on an 8 km EPSG3031 polar stereographic common grid for ISMIP6. Variable names are the same as those used for ISMIP6 i.e: land ice mass (lim), land ice mass above floatation (limnsw), floating area (iareaf), grounded area (iareag), thickness (lithk), x component of mean velocity (xvelmean), y component of mean velocity (yvelmean), basal mass flux (libmassbffl), acabf (surface mass balance), sftflf (floating ice mask), sftgrf (grounded ice mask), sftgif (ice mask), dlithkdt (ice thickness imbalance), base (elevation at base of ice sheet) and orog (surface elevation of ice sheet). These latter two are only included for the experiments plotted in Figure 11 in "ISMIP6-based Antarctic Projections to 2100: simulations with the BISICLES ice sheet model in the Cryosphere".   Also included are csv data for summary variables, masked regionally, and by sectors detailed in the main paper. Please contact J ONeill with any questions or requests.

Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"

The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers. This dataset provides the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.   Abstract:  One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset. We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.

opencc-by-4.0Oct 2024View details →

Companion data artifacts: Technical framework demonstration for deep learning-based wood species classification with advanced sub-μ-CT imaging

This is the companion data artifact collection for the IWAWA paper manuscript by Jannik Stebani, Tim Lewandrowski, Kilian Dremel, Simon Zabler and Volker Haag.It is generally to be used with the visualization and prediction showcases implemented in the Binder notebooks launched from this <a href="https://github.com/stebix/woodnet-showcase" target="_blank" rel="noopener">woodnet-showcase</a> GitHub repository. The artifacts amount to the following: <ol> <li><code>acer-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a Acer pseudoplatanus sub-μ-CT scan</li> <li><code>pinus-artifact.hdf5</code> : Exemplary <code>(256, 256, 256)</code> subvolume from a Pinus sylvestris sub-μ-CT scan</li> <li><code>weights-artifact.pth</code> : Exemplary PyTorch trained weights for a woodnet/deep neural network to demonstrate classification of the above samples</li> </ol>

Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures

This dataset comes from the following paper: Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a> It contains: <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>

opencc-by-4.0Oct 2024View details →

The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"

The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK". Data collector: Runda Zheng

Linked collectors and determiners for: Flora Sumatra: Digitizing and data basing specimens of the Sumatran Flora deposited at Herbarium Universitas Andalas (ANDA)-Part 2.

Natural history specimen data linked to collectors and determiners held within, "Flora Sumatra: Digitizing and data basing specimens of the Sumatran Flora deposited at Herbarium Universitas Andalas (ANDA)-Part 2". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/39e85504-1ebe-4671-be65-19ccdc1d7c7d">https://bionomia.net/dataset/39e85504-1ebe-4671-be65-19ccdc1d7c7d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/39e85504-1ebe-4671-be65-19ccdc1d7c7d">https://gbif.org/dataset/39e85504-1ebe-4671-be65-19ccdc1d7c7d</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Taxonomic revision of the speckled crabs, genus Arenaeus Dana, 1851 (Brachyura: Portunidae) based on morphological and molecular data.

Natural history specimen data linked to collectors and determiners held within, "Taxonomic revision of the speckled crabs, genus Arenaeus Dana, 1851 (Brachyura: Portunidae) based on morphological and molecular data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5e87f718-703c-4280-8ca9-f8b6d0f3b402">https://bionomia.net/dataset/5e87f718-703c-4280-8ca9-f8b6d0f3b402</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5e87f718-703c-4280-8ca9-f8b6d0f3b402">https://gbif.org/dataset/5e87f718-703c-4280-8ca9-f8b6d0f3b402</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Literature based species occurrence data of birds of North-East India.

Natural history specimen data linked to collectors and determiners held within, "Literature based species occurrence data of birds of North-East India". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6115006c-ec2f-4bfd-9315-115879785446">https://bionomia.net/dataset/6115006c-ec2f-4bfd-9315-115879785446</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6115006c-ec2f-4bfd-9315-115879785446">https://gbif.org/dataset/6115006c-ec2f-4bfd-9315-115879785446</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Aix-Marseille Université - MARS herbarium – Cytogenetic data-base.

Natural history specimen data linked to collectors and determiners held within, "Aix-Marseille Université - MARS herbarium – Cytogenetic data-base". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32">https://bionomia.net/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32">https://gbif.org/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32</a>. Formatted as a Frictionless Data package.