Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
25
datasets available to search
ShareScore release 0.9.0
Dataset results
25 results for “covalent organic framework”
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP
<p>The dataset uploaded herein is associated with the paper published under the title "<i>Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications</i>" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.</p><p><strong>Abstract</strong></p><p>Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.</p>
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
Electronic Structure Data for "Design of Covalent Organic Frameworks through on-the-fly Batch-based Bayesian Optimization"
<p>This is a dataset of 1736 potential building blocks for the construction of covalent organic frameworks (COF). Electronic structures were calculated with the GFN1-xTB tight binding DFT approach as implemented in the xTB package (v6.2.3). The dataset contains all necessary inputs and outputs from these calculations. Structures were optimised with xTB's internal normal coordinate rational function optimizer (ANCopt) at the default geometry convergence criterion.</p> <p>The dataset contains calculations for two major parameters determining the suitability of the resulting COFs as an organic semiconductor, specifically, the approximate energy alignment of the homo level and the reorganization free energy.</p>
Datasets of Antiaromatic COFs associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes"
<p>X-ray scattering and sorption data associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes". X-ray scattering data is provided for COF and POP materials, including data from stability tests, as three-column ascii files with columns q (nm<sup>-1</sup>), I (m<sup>-1</sup>) and uncertainty on I, as well as being provided in 2θ.</p>
Datasets of evaluated Covalent Organic Frameworks for photocatalysis
<p>We developed a workflow to evaluate covalent organic frameworks (COFs) for photocatalysis. As an input, we chose the CURATED COFs (doi:<a href="https://doi.org/10.24435/materialscloud:z6-jn">10.24435/materialscloud:z6-jn</a>), a database of experimental COFs. We performed density functional theory (DFT) calculations, and post-processed the result with in-house codes available on GitHub (see <a href="https://github.com/bmourino/cof_photocatalysis">GitHub repository</a>). The dataset in cofs_descriptors.csv is the full dataset with all of our computed descriptors, and an application was developed to allow for interactively exploring the results (see <a href="https://github.com/bmourino/cofs_photocat_app">GitHub repository</a>). The bootstrapped_statistics.csv is the result of the statistical analysis with DABEST (Data Analysis with Bootstrap-coupled ESTimation, doi:<a href="https://doi.org/10.1038/s41592-019-0470-3">10.1038/s41592-019-0470-3</a>).</p>
Photoelectrochemical water splitting by triazine based covalent organic framework
<p>DFTB-optimised geometries of TFBB-TAB and TFBB-TAT as cif files.</p>
Effect of Guest Molecules on the Stacking Configuration of Covalent Organic Frameworks: A Periodic Energy Decomposition Analysis
<p>Illustrative videos that provide an in-depth understanding of the methodology and the result obtained from our recent study. A full description of the method is found in the paper. The potential energy surface was plotted using imshow Matplotlib and the 3d structures were rendered using povray. The videos are all free to use for educational purposes but please remember to cite the paper at anytime you use the videos. </p> <p> </p>
On-Surface Synthesis of Disilabenzene-Bridged Covalent Organic Frameworks
<p>Here you will find the list of folders and their contents used to generate the data and the figures published on "On-Surface Synthesis of Disilabenzene-Bridged Covalent Organic Frameworks" paper</p> <p><strong>On SiCOF_data.zip you will find the following folders:</strong><br> <strong>2D_network_on_au:</strong><br> outcar_k3.fin: VASP output file. Contains the DFT parameters used to relax the Si-COF network geometry on gold<br> poscar_k3.fin: VASP geometry file. This is the relaxed geometry of the Si-COF network on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si-COF network on gold<br> <strong>C4Si2_ribb_on_au:</strong><br> outcar.fin: VASP output file. Contains the DFT parameters used to relax the Si ribbon 1 geometry on gold<br> poscar.fin: VASP geometry file. This is the relaxed geometry of the Si ribbon 1 on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si ribbon 1 network on gold<br> <strong>C4Si_ribb_on_au:</strong><br> outcar.fin: VASP output file. Contains the DFT parameters used to relax the Si ribbon 2 network geometry on gold<br> poscar.fin: VASP geometry file. This is the relaxed geometry of the Si ribbon 2 network on gold obtained in the previous calculation<br> output_aims.txt: FHI-aims output file. Contains the DFT parameters used to print the eigenvectors of the Si ribbon 2 network on gold<br> <strong>critic2</strong>:<br> - Example of input files to simulate constant current (cc) and constant height (ch) stm using the Tersoff-Hamann approximation through the critic2 code:<br> stm_cc.inp stm_ch.inp<br> <strong>NICs</strong>:<br> - ORCA output files. Contains the DFT parameters and geometries used to calculate the nucleus independent nuclear shift (NICs) of several molecules:<br> 2D-buckled.out<br> benzene.out<br> disilahexa.out<br> mol2Br.out<br> 2D-flat.out<br> Brsila.out<br> mol2Br+2Au.out<br> silaben.out<br> <strong>PPSTM</strong>:<br> params.ini: control file for the PPM (PP-AFM) code, that is used to calculate the position of oxygen for the relaxed STM scan calculated by PP-STM<br> <strong>PPSTM_simple.py</strong>: Script running PP-STM simulations with 13% of s and 87% of pxy orbitals (one possibility for simulating CO tip)</p> <p><strong>On Source_data.zip you will find all the unprocessed images used in the paper. Additionally, it is included in CHGCAR_files.zip the charge densities used to generate the supplementary figure 5. </strong></p> <p><strong>Version of the softwares and workflow on the PP-STM:</strong><br> FHI-aims version ( https://aimsclub.fhi-berlin.mpg.de ) was aims.191119.mpi.scalapack.x .<br> cirtic2: https://aoterodelaroza.github.io/critic2/examples/example_14_01_stmqe/<br> PPM (PP-AFM) version was a master version from Nov 4, 2021: https://github.com/ProkopHapala/ProbeParticleModel/commit/327c61cdbd348307c5255c4618f12d28f4ababd5<br> PP-STM version was a master version from Nov 16, 2021: https://github.com/Probe-Particle/PPSTM/commit/4434739bd737e58a2fc556dff24e8e7d6eab084e</p> <p>The workflow for the PP-STM (CO-tip STM) images was as follows:<br> Using the poscar*.fin for creating the geometry.<br> Run a single point (no optimization) calculation with FHI-aims for creating the hartree potential ("cube_001_hartree_potential.cube") and <br> then with control.in and PPSTM_simple.py file in the folder and with properly set way to<br> PP-AFM folder and PP-STM path (in the top of the PPSTM_simple.py file) running following commands in command line:</p> <p>python3 PPAFM_PATH/generate_LJFF.py -i cube_001_hartree_potential.cube<br> python3 PPAFM_PATH/generate_ElFF.py -i cube_001_hartree_potential.cube # these will create force-field for PP-AFM calculations #<br> python3 PPAFM_PATH/relaxed_scan.py --pos # this will create position of Probe Particle (simulating oxygen postions ) for the STM scan #<br> python3 PPSTM_simple.py # will create the PPSTM images #</p>
Metadata - Tetrathiafulvalene-based covalent organic framework as high-voltage organic cathodes for lithium batteries
Open the record for dataset details and reuse information.
Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres
<p>This repository contains the source data for the manuscript <strong>"Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres" </strong></p> <p>The statistical model of 50 layers, 4-6 layers for TpDPP, TpTta, and TpTab.</p> <p>A code for building the statistical structure for layered materials by stacking layers following the Maxwell-Boltzmann energy distribution of their stacking modes. Full Changelog: <a href="https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1">https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1</a></p>
Data from: Magnetic covalent organic frameworks (mcofs): A sustainable solution for emerging organic contaminants (Eocs) from the river
Open the record for dataset details and reuse information.
Solid-state nuclear magnetic resonance data from: Covalent organic frameworks for carbon dioxide capture from air
Open the record for dataset details and reuse information.
Solid-state NMR data for the publication "Bonding of Polyethylenimine in Covalent Organic Frameworks for CO2 Capture from Air"
<p>This upload contains the solid-state NMR of COF-709 series obtained by using Bruker Avance IV NEO 400 MHz NMR instrument. The instrument used in this work was supported by the National Science Foundation under Grant No. 2018784. Per NSF requirements, the data will be publicly available at the time of publication. This dataset can be opened and processed by using MestreNova NMR or similar softwares.</p>
Optimized structures of imine-linked donor-acceptor covalent organic frameworks
<p>Structures of covalent organic frameworks (COFs) optimized using PBE+D3. Includes the pure COFs, protonated versions (with ascorbate as the anion), and intermediates of the oxygen evolution and hydrogen evolution reactions. The COFs were experimentally described by Yang et al., Nature Commun. 13, 6317, 2022 (DOI: <a title="Externen Link im neuen Fenster öffnen" href="http://doi.org/10.1038/s41467-022-33875-9" target="_top">10.1038/s41467-022-33875-9</a>).</p> <p>This set of files is part of a manuscript published in Phys. Chem. Chem. Phys: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CP02019G">https://doi.org/10.1039/D4CP02019G</a></p>
ML_AB datasets - Hydorated covalent organic framework
<p>RPBE+D3 datasets for hydrated COFs in the VASP ML_AB format. It includes reference structures, energy, forces, and the stress tensor.</p> <table> <tbody> <tr> <td>File name</td> <td>COF</td> <td>side chain length</td> <td>water uptake</td> </tr> <tr> <td>ML_AB_S3_0_1</td> <td>COF-S3</td> <td>0</td> <td>1</td> </tr> <tr> <td>ML_AB_S3_0_2</td> <td>COF-S3</td> <td>0</td> <td>2</td> </tr> <tr> <td>ML_AB_S3_0_4</td> <td>COF-S3</td> <td>0</td> <td>4</td> </tr> <tr> <td>ML_AB_S3_1_1</td> <td>COF-S3</td> <td>1</td> <td>1</td> </tr> <tr> <td>ML_AB_S3_1_2</td> <td>COF-S3</td> <td>1</td> <td>2</td> </tr> <tr> <td>ML_AB_S3_2_1</td> <td>COF-S3</td> <td>2</td> <td>1</td> </tr> <tr> <td>ML_AB_S3_2_2</td> <td>COF-S3</td> <td>2</td> <td>2</td> </tr> <tr> <td>ML_AB_S3_3_2</td> <td>COF-S3</td> <td>3</td> <td>2</td> </tr> <tr> <td>ML_AB_S6_0_05</td> <td>COF-S6</td> <td>0</td> <td>0.5</td> </tr> <tr> <td>ML_AB_S6_0_1</td> <td>COF-S6</td> <td>0</td> <td>1</td> </tr> <tr> <td>ML_AB_S6_0_2</td> <td>COF-S6</td> <td>0</td> <td>2</td> </tr> <tr> <td>ML_AB_S6_1_05</td> <td>COF-S6</td> <td>1</td> <td>0.5</td> </tr> <tr> <td>ML_AB_S6_1_1</td> <td>COF-S6</td> <td>1</td> <td>1</td> </tr> <tr> <td>ML_AB_S6_1_2</td> <td>COF-S6</td> <td>1</td> <td>2</td> </tr> <tr> <td>ML_AB_S6_2_1</td> <td>COF-S6</td> <td>2</td> <td>1</td> </tr> <tr> <td>ML_AB_S6_3_1</td> <td>COF-S6</td> <td>3</td> <td>1</td> </tr> </tbody> </table> <p> </p>
Expanding the Diversity of Linkages in Covalent Organic Frameworks through Staudinger Cycloaddition: Synthesis of cis-β-Lactam and cis-β-Thiolactam COFs
Open the record for dataset details and reuse information.
PFAS Adsorption Using Viologen-Modified Covalent Organic Frameworks: Synergistic Adsorption Mechanisms Provide a Robust Approach for Water Purification
<p>Dataset for manuscript.</p>
Active learning molecular dynamics-assisted insights into ultralow thermal conductivity of two-dimensional covalent organic frameworks
<p>This is a training sets for monolayer COF-5, which could be used for NEP trainging.</p>
Metadata - Tuning the electrochemical performance of covalent organic framework cathodes for Li- and Mg-based batteries: the influence of electrolyte and binder
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.