cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dcfy5
title: CCDC 2215537: Experimental Crystal Structure Determination

description: Related Article: Natalia Terenti, Elena Melnic, Victor Fruth, Natalia Nedelko, Pavlo Aleshkevych, Sabina Lewińska, Anna Ślawska-Waniewska, Victor Ch. Kravtsov, Ana Lazarescu, Vasile Lozan|2023|J.Solid State Chem.|324|124108|doi:10.1016/j.jssc.2023.124108
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1h99
title: CCDC 2235760: Experimental Crystal Structure Determination

description: Related Article: Xiaoyan Xin, Yudai Zhou, Kai Zhang, Zhengxu Cai, Jianbing Shi, Bin Tong, Yuping Dong|2023|Dyes Pigm.|217|111412|doi:10.1016/j.dyepig.2023.111412
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fkzww
title: CCDC 2251619: Experimental Crystal Structure Determination

description: Related Article: Lata Panicker|2023|J.Mol.Struct.|1288|135773|doi:10.1016/j.molstruc.2023.135773
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.23393915
title: MR of IC and BPH

description: MR of IC and BPH
DOI: 10.6084/m9.figshare.23393915.v1
title: MR of IC and BPH

description: MR of IC and BPH
DOI: 10.6084/m9.figshare.23394107
title: All_ecth_yr_.csv

description: Data related to publication "Disentangling the deforestation-environmental crime nexus in Latin America". See publication for details/variables explanation
DOI: 10.6084/m9.figshare.23393780
title: CITAS A Y B ROSA MARIA GUTIERREZ RIOS

description: SE PRESENTAN LAS CITAS GLOBALES TIPO A Y B COLECTADAS DE WEB SCIENCE Y SCOPUS DE ROSA MARIA GUTIERREZ RIOS
DOI: 10.6084/m9.figshare.23395526
title: Supplemental Material.docx

description: Sound velocity of eclogite at high pressures and implications for detecting eclogitization in subduction zones
DOI: 10.6084/m9.figshare.23395526.v1
title: Supplemental Material.docx

description: Sound velocity of eclogite at high pressures and implications for detecting eclogitization in subduction zones
DOI: 10.5281/zenodo.7632801
title: Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections

description: The data is organized in 32 files: collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ... 15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt. 15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.
DOI: 10.5281/zenodo.7732514
title: Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections

description: The data is organized in 32 files: collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ... 15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt. 15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.
DOI: 10.5281/zenodo.8017995
title: Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections

description: The data is organized in 32 files: collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ... 15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt. 15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.
DOI: 10.5281/zenodo.7632800
title: Dataset of Linkability Networks of Ethereum Accounts Involved in NFT trading of Top 15 NFT Collections

description: The data is organized in 32 files: collection_metadata.txt stores basic information about each collection that was analyzed. The graph data of the collection is stored in a file named as the nickname of the collection (slug column in collection_metadata.txt). The most important columns/properties are: rank, slug – nickname, creation date, address, and volume data, ... 15 files reporting NFT ownership transfer, the names corresponding to the nicknames of the collections in collection_metadata.txt. 15 files with graph data, the names correspond to the nicknames of the collections in collection_metadata.txt. Each file gives the address linkage graph of one of the top 15 collections according to monetary volume on Opensea marketplace, computed as presented in methodology, exclusively on Ethereum blockchain.
DOI: 10.6084/m9.figshare.23395856
title: Supplemental Material.docx

description: Sound velocity of eclogite at high pressures and implications for detecting eclogitization in subduction zones
DOI: 10.6084/m9.figshare.23395856.v1
title: Supplemental Material.docx

description: Sound velocity of eclogite at high pressures and implications for detecting eclogitization in subduction zones
DOI: 10.21227/qe6b-g385
title: Data for "Driver-pedestrian interactions at unsignalized crossings are not in line with the Nash equilibrium"

description: The following dataset provides all the data that were used for analysis and modeling in the paper entitled "Driver-pedestrian interactions at unsignalized crossings are not in line with the Nash equilibrium". The file comes with a description so one can replicate the analyses and computational modeling using the information here and in the paper itself. The data contain road user kinematic, demographic, and personality trait information.  If you use this dataset, please cite: Kalantari, A. H., Yang, Y., Merat, N., Lee, Y.M., & Markkula, G. (2023). Modelling vehicle-pedestrian interactions at unsignalised locations: Road users may not play the Nash equilibrium. https://psyarxiv.com/axseu/
DOI: 10.6092/unibo/amsacta/7218
title: The NeRF-Stereo Dataset

description: The dataset contains several image sequences collected with mobile phones and the corresponding image triplets and disparity labels for training deep stereo networks effortlessly and without any ground-truth. By leveraging state-of-the-art neural rendering solutions, we generate stereo training data from image sequences collected with a single handheld camera. On top of them, a NeRF-supervised training procedure is carried out, from which we exploit rendered stereo triplets to compensate for occlusions and depth maps as proxy labels. This results in stereo networks capable of predicting sharp and detailed disparity maps.
DOI: 10.5281/zenodo.6556541
title: ReSpa - Towards an automatic requirements classification in a new Spanish dataset

description: ReSpa (Spanish Dataset for requirements classification) dataset is conformed by requirements collected from final degree projects from one University. It was presented in the paper 'Towards an automatic requirements classification in a new Spanish dataset' and used also in the paper 'Requirements Classification Using FastText and BETO in Spanish Documents'. Cited as: Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022, August). Towards an automatic requirements classification in a new Spanish dataset. In <em>2022 IEEE 30th International Requirements Engineering Conference (RE)</em> (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039
DOI: 10.5281/zenodo.6556540
title: ReSpa - Towards an automatic requirements classification in a new Spanish dataset

description: ReSpa (Spanish Dataset for requirements classification) dataset is conformed by requirements collected from final degree projects from one University. It was presented in the paper 'Towards an automatic requirements classification in a new Spanish dataset' and used also in the paper 'Requirements Classification Using FastText and BETO in Spanish Documents'. Cited as: Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022, August). Towards an automatic requirements classification in a new Spanish dataset. In <em>2022 IEEE 30th International Requirements Engineering Conference (RE)</em> (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039
DOI: 10.5281/zenodo.8017900
title: Tracking and blocking interdependencies of cellular BRAF-MEK oncokinase activities

description: The selective targeting of mutated kinases in cancer therapies has the potential to improve the therapeutic success and thereby the survival of patients. In the case of melanoma, the constitutively active MAP kinase pathway is targeted by combinatorial inhibition of BRAF and MEK activities. These MAPK pathway players may display patient-specific differences in the onco-kinase mutation spectrum, which needs to be considered for the design of more efficient personalized therapies. Here, we extend a bioluminescence-based kinase conformation biosensor (KinCon) to allow for live-cell tracking of interconnected kinase activity states. First, we show that common MEK1 patient mutations promote a structural rearrangement of the kinase to an opened and active conformation. This effect was reversible by binding of MEK inhibitors to mutated MEK1, as shown in biosensor assays and molecular dynamics simulations. Second, we implement a novel application of the KinCon technology for tracking the simultaneous, vertical targeting of the two functionally linked kinases BRAF and MEK1. Thus, we demonstrate that, in the presence of constitutively active BRAF-V600E, specific inhibitors of both kinases are efficient in driving MEK1 into a closed, inactive conformation state. We compare current melanoma treatments and show that combinations of BRAFi and MEKi displayed a more pronounced structural change of the drug sensor than the respective single agents, thereby identifying synergistic effects among these drug combinations. In summary, we depict the extension of the KinCon biosensor technology to systematically validate, anticipate, and personalize tailored drug arrangements using a multiplexed setup.
DOI: 10.5281/zenodo.8017899
title: Tracking and blocking interdependencies of cellular BRAF-MEK oncokinase activities

description: The selective targeting of mutated kinases in cancer therapies has the potential to improve the therapeutic success and thereby the survival of patients. In the case of melanoma, the constitutively active MAP kinase pathway is targeted by combinatorial inhibition of BRAF and MEK activities. These MAPK pathway players may display patient-specific differences in the onco-kinase mutation spectrum, which needs to be considered for the design of more efficient personalized therapies. Here, we extend a bioluminescence-based kinase conformation biosensor (KinCon) to allow for live-cell tracking of interconnected kinase activity states. First, we show that common MEK1 patient mutations promote a structural rearrangement of the kinase to an opened and active conformation. This effect was reversible by binding of MEK inhibitors to mutated MEK1, as shown in biosensor assays and molecular dynamics simulations. Second, we implement a novel application of the KinCon technology for tracking the simultaneous, vertical targeting of the two functionally linked kinases BRAF and MEK1. Thus, we demonstrate that, in the presence of constitutively active BRAF-V600E, specific inhibitors of both kinases are efficient in driving MEK1 into a closed, inactive conformation state. We compare current melanoma treatments and show that combinations of BRAFi and MEKi displayed a more pronounced structural change of the drug sensor than the respective single agents, thereby identifying synergistic effects among these drug combinations. In summary, we depict the extension of the KinCon biosensor technology to systematically validate, anticipate, and personalize tailored drug arrangements using a multiplexed setup.
DOI: 10.34740/kaggle/dsv/5876718
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.5281/zenodo.6507185
title: Available datasets of Aquilegia oxysepala for this project

description: Available datasets of <em>Aquilegia oxysepala</em> for this project
DOI: 10.5281/zenodo.6507186
title: Available datasets of Aquilegia oxysepala for this project

description: Available datasets of <em>Aquilegia oxysepala</em> for this project
DOI: 10.5281/zenodo.8018011
title: Available datasets of Aquilegia oxysepala for this project

description: Available datasets of <em>Aquilegia oxysepala</em> for this project
DOI: 10.5281/zenodo.7423327
title: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery

description: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery (https://zenodo.org/record/7423328)
description: Added with more input files (https://zenodo.org/record/7423328)
DOI: 10.5281/zenodo.7423328
title: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery

description: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery
DOI: 10.5281/zenodo.8018016
title: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery

description: Molecular dynamics simulation input files: Histone Tail Electrostatics Modulate E2-E3 Enzyme Dynamics: A Gateway to Regulate Ubiquitination Machinery (https://zenodo.org/record/7423328)
description: Added with more input files (https://zenodo.org/record/7423328)
DOI: 10.5281/zenodo.3476746
title: Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films

description: Transmission electron microscopy data used in the journal publication "Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films" <strong>Data files</strong> There are two data types: Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 Acquired on a probe corrected Jeol ARM200CF Acceleration voltage: 200 kV Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring) Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring) Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5 The data can be loaded in python using h5py. For the Medipix3 data: <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> For the STEM-ADF or STEM-ABF data: <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem: <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling: <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <br> <strong>Processing files</strong> All the TEM data has been processed using python scripts, which is named based on the type of processing: d00N_...: Medipix3 data processing a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures. The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work. To setup the python environment with the required libraries, and run all the scripts: <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre>
DOI: 10.5281/zenodo.3476745
title: Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films

description: Transmission electron microscopy data used in the journal publication "Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films" <strong>Data files</strong> There are two data types: Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 Acquired on a probe corrected Jeol ARM200CF Acceleration voltage: 200 kV Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring) Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring) Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5 The data can be loaded in python using h5py. For the Medipix3 data: <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> For the STEM-ADF or STEM-ABF data: <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem: <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling: <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <br> <strong>Processing files</strong> All the TEM data has been processed using python scripts, which is named based on the type of processing: d00N_...: Medipix3 data processing a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures. The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work. To setup the python environment with the required libraries, and run all the scripts: <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre>
DOI: 10.5517/ccdc.csd.cc2f1hbb
title: CCDC 2235761: Experimental Crystal Structure Determination

description: Related Article: Xiaoyan Xin, Yudai Zhou, Kai Zhang, Zhengxu Cai, Jianbing Shi, Bin Tong, Yuping Dong|2023|Dyes Pigm.|217|111412|doi:10.1016/j.dyepig.2023.111412
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f1hcc
title: CCDC 2235762: Experimental Crystal Structure Determination

description: Related Article: Xiaoyan Xin, Yudai Zhou, Kai Zhang, Zhengxu Cai, Jianbing Shi, Bin Tong, Yuping Dong|2023|Dyes Pigm.|217|111412|doi:10.1016/j.dyepig.2023.111412
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2flrng
title: CCDC 2252356: Experimental Crystal Structure Determination

description: Related Article: Benjamin L. L. Réant, Dukula De Alwis Jayasinghe, Ashley J. Wooles, Stephen T. Liddle, David P. Mills|2023|Dalton Trans.|52|7635|doi:10.1039/D3DT00987D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2flrph
title: CCDC 2252357: Experimental Crystal Structure Determination

description: Related Article: Benjamin L. L. Réant, Dukula De Alwis Jayasinghe, Ashley J. Wooles, Stephen T. Liddle, David P. Mills|2023|Dalton Trans.|52|7635|doi:10.1039/D3DT00987D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/5876723
title: Arabic Poetry Emotions Dataset
DOI: 10.5281/zenodo.8018057
title: Prelimnary analysis of the datset through Power BI

description: The dataset is been cleaned and visualised using the Microsoft Power BI tool.