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1,294 results for “reactions”
Human Behavioral Reaction Collection
<p>This dataset collects human behavioral reactions to robotic failures. This data was recorded from a user study and has been processed for anonymization. The dataset.csv contains human responses in terms of facial emotional values of the participants for the robot failures as they collaborated with a Baxter robot in a HRC task. The task details are defined in the papers:</p> <div> <h2>Citation</h2> </div> <p>If you use this dataset, please cite the following papers:</p> <ol> <li>P. Khanna, E. Yadollahi, M. Bj¨orkman, I. Leite, and C. Smith, “Effects of explanation strategies to resolve failures in human-robot collaboration,” in IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2023, pp. 1829–1836</li> <li>P. Khanna, E. Yadollahi, M. Bj¨orkman, I. Leite, and C. Smith, “User study exploring the role of explanation of failures by<br>robots in human robot collaboration tasks,” in The Imperfectly Relatable Robot: An interdisciplinary workshop on the role of failure in HRI, Stockholm, Sweden, Mar. 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2303.16010</li> </ol> <p>@misc{khanna2023userstudyexploringrole,<br> title={User Study Exploring the Role of Explanation of Failures by Robots in Human Robot Collaboration Tasks}, <br> author={Parag Khanna and Elmira Yadollahi and Mårten Björkman and Iolanda Leite and Christian Smith},<br> year={2023},<br> eprint={2303.16010},<br> archivePrefix={arXiv},<br> primaryClass={cs.RO},<br> url={https://arxiv.org/abs/2303.16010}, <br>}</p> <p> </p> <p>@misc{khanna2025reflexdatasetmultimodaldataset,</p> <p> title={REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robot Failures and Explanations}, <br> author={Parag Khanna and Andreas Naoum and Elmira Yadollahi and Mårten Björkman and Christian Smith},<br> year={2025},<br> eprint={2502.14185},<br> archivePrefix={arXiv},<br> primaryClass={cs.RO},<br> url={https://arxiv.org/abs/2502.14185}, <br>}</p> <p> </p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
Machine Learning Quantum Reaction Rate Constants
<p>Dataset of 1,517,419 quantum reaction rate constant products <span class="math-tex">\(k^{\text {QM}}(T)Q_{\text R}(T)\)</span> computed from the transmission coefficient for model single and double barrier minimum energy paths. Here <span class="math-tex">\(k^{QM}(T) \)</span> is the quantum reaction rate constant at temperature <span class="math-tex">\(T\)</span> and <span class="math-tex">\(Q_\text{R}(T)\)</span> is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict <span class="math-tex">\(\log{k^{\text{QM}}(T)Q_\text{R}(T)}\)</span>.</p> <p><strong>Cite as</strong></p> <p>Please cite the following references when using this dataset:</p> <p>[1] E. Komp and S. Valleau, Machine Learning Quantum Reaction Rate Constants, <em>J. Phys. Chem. A</em>, 124:8607–8613, 2020, <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jpca.0c05992">doi: 10.1021/acs.jpca.0c05992</a>.</p> <p>[2] E. Komp and S.Valleau, Machine Learning Quantum Reaction Rate Constants (1.0.0) [Data set], 2020, <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.5510392">https://doi.org/10.5281/zenodo.5510392 </a></p> <p><strong>Contents</strong></p> <p>Descriptions of entries in the tabular dataset file `QM_kQ.csv`. Please refer to the publication [1] for details.</p> <ul> <li>`mass_au`: Mass of the reactants in atomic units. </li> <li>`width_1_au`: Width of first potential energy barrier in atomic units.</li> <li>`width_2_au`: Width of second (if present) potential barrier in atomic units. For single barriers width_2_au = 0.0.</li> <li>`height_1_au`: Activation energy of first potential energy barrier in atomic units.</li> <li>`height_2_au`: Activation energy of second (if present) potential energy barrier in atomic units. For single barriers height_2_au = 0.0.</li> <li>`dist_au`: For double barriers, absolute value of the difference between the position of the two potential energy maxima along the reaction coordinate in atomic units. For single barriers dist_au = 0.0.</li> <li>`alpha_symm`: Symmetry constant for single barriers, defined as the difference between product and reactant energies in atomic units.</li> <li>`alpha_double`: Symmetry constant for double barriers, defined as the sum of the normalized difference between barrier heights and the normalized difference between barrier widths, unitless.</li> <li>`slope`: Slope of the first reaction barrier along the reaction coordinate in atomic units. Slope values were evaluated numerically from the type of potential energy barrier see Ref [1] SI.</li> <li>`temp_K`: Temperature in Kelvin.</li> <li>`kQ_rate`: Quantum reaction rate constant times reactant partition function in units of 1/ps. </li> <li>`log_kQ_rate`: Natural logarithm of the quantum reaction rate constant times the reactant partition function in units of log(1/ps).</li> </ul>
S74 | REFTPS | Transformation Products and Reactions from Literature
<p>This is the collection associated with list S74 REFTPS Transformation Products and Reactions from Literature on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>This dataset is designed to provide an entry point for users to contribute transformation products and reactions documented in the literature for addition to the NORMAN SLE, SusDat and the PubChem Transformations section.</p> <p>Change logs and version tracking at the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem/-/tree/master/annotations/tps/REFTPS">ECI GitLab site</a>.</p> <p>Change log: v0.0.2 added InChIKey file. v0.1.0 added new reactions from Anca Baesu and DTXSIDs. v0.2.0 added PFAS TPs from Parviel Chirsir. v0.2.1 more PFAS TPs from Parviel. v0.3.0 Emma added HMMM TPs; v0.3.1 updated references and added new CIDs; added new MS/MS file. v0.4.0 new PFAS TPs plus MS/MS and NMR. v0.4.1 new CID added, plus CID 67543 updated to 14571268. v0.5.0 new 8:2 FT TPs plus annotation data; new structures. v0.5.1 added new CIDs. v0.5.2 added 2:2 to 6:2 FT TPs, updated ref for Bugsel. v0.5.3: added new CIDs. v0.6.0 added new structures. v0.7.0 added more new structures. v 0.7.1: updated CIDs in substances, fixed PFHpA mapping in transformations (some were mismapped to CID 67819). v 0.7.2: updated Biosystem description for many records. v0.8.0: updated CID 163201609 => 166001338, adjusted last 4 MS/MS, added Barisci AOP transformations. v0.9.0 added new structures. v0.9.1 updated CIDs and added radical structures from deposition. v0.10.0 added new irgarol reaction; v0.10.1 added new CID. v0.11.0 added Avendano and Mabury transformations from Parviel. v0.12.0 added Washington MS/MS and Marjanovic MS/MS and reactions. v0.13.0 added Galaxolide transformation. v0.14.0 added Zweigle PFAS TPs with MSMS. v0.14.1 added new CIDs. v0.15.0 added antibiotic TPs from Paul Löffler, SLU, incl. entries with no CID. v0.15.1 added new CIDs. v0.16.0 added benzothiazole reactions. v0.17.0 added TooCOLD TPs from Rick. v0.18.0 added new TFA reactions. v0.19.0 added pak choi reactions, several with no CID. v0.19.1 added new CIDs. v0.20.0 added dimers from Li Ji. v0.20.1 added new CIDs. v0.21.0 added the EJ Weber PFAS libraries "EnvLib" and "MetaLib", curation by Parviel and Emma. Some new CIDs to come. v0.21.1: fixed char issues in substance & transformation files. v0.21.2 added new CIDs. v0.22.0 added "Class_parent" column to the substance deposition file to aid annotation. v0.23.0: added Parviel's zebrafish entries</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity
<p>The data present in this repository reflect intermediate and processed data presented in the manuscript, <em>Induced immune reaction in the acorn worm, Saccoglossus kowalevskii, informs the evolution of antiviral immunity. </em>This manuscript is still under review; as such, this page will be updated upon publication.</p> <p> </p> <p><strong>Manuscript Abstract:</strong></p> <p>Evolutionary perspectives on the deployment of immune factors following infection have been shaped by studies on a limited number of biomedical model systems with a heavy emphasis on vertebrate species. Though their contributions to contemporary immunology cannot be understated, a broader phylogenetic perspective is needed to understand the evolution of immune systems across Metazoa. In our study, we leverage differential gene expression analyses to identify genes implicated in the antiviral immune response of the acorn worm hemichordate, <em>Saccoglossus kowalevskii</em>, and place them in the context of immunity evolution within deuterostomes – the animal clade composed of chordates, hemichordates, and echinoderms. Following acute exposure to the synthetic viral dsRNA analog, poly(I:C), we show that <em>S. kowalevskii </em>responds by regulating the transcription of genes associated with canonical innate immunity signaling pathways (e.g., NF-κB and IRF signaling) and metabolic processes (e.g., lipid metabolism), as well as many genes without clear evidence of orthology with those of model species. Aggregated across all experimental time point contrasts, we identify 423 genes that are differentially expressed in response to poly(I:C). We also identify 147 genes with altered temporal patterns of expression in response to immune challenge. By characterizing the molecular toolkit involved in hemichordate antiviral immunity, our findings provide vital evolutionary context for understanding the origins of immune systems within Deuterostomia.</p> <p> </p> <p><strong>Repository contents:</strong></p> <p>### Processed Data ###</p> <ul> <li><em>Full_DESeq2_matrix.csv </em>--> DESeq2 results for each contrast (e.g., 2hpi treatment vs. control)</li> <li><em>MaSigPro.Clusters.csv</em> --> Mean expression for each gene placed within a pDEG cluster</li> <li><em>MaSigPro.SigGenes.TreatmentvsControl.Robj</em> --> T.fit() R-object output from MaSigPro pipeline. This can be opened in R using the load() function.</li> </ul> <p>### Homology Assessment ###</p> <ul> <li><em>Orthofinder.tar.gz</em> --> OrthoFinder results</li> <li><em>Skowalevskii_Genome_Annotation.SPHuman_and_HOG.csv</em> --> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-Human" and "HOG" output described in the main text of the paper</li> <li><em>Skowalevskii_Genome_Annotation.SPPANTHER.csv </em>--> Assignment of IDs to Skow1.1 genes conforming to "PANTHER-SwissProt" output described in the main text of the paper</li> </ul> <p>### Functional Annotation ###</p> <ul> <li><em>Skow.HMMER_Pfam.domtblout.tsv</em> --> Pfam annotation of the Skow1.1 genome assembly in HMMER's domblout format</li> <li><em>Skow.KofamKOALA.detail.tsv</em> --> KO annotation of the Skow1.1 genome assembly using KofamKOALA (detailed output)</li> <li><em>Skow.KofamKOALA.detail.tsv </em>--> KO annotation of the Skow1.1 genome assembly using KofamKOALA (mapper output)</li> <li><em>SkowAnnotations.GO.tsv</em> --> GO annotation of the Skow1.1 genome assembly</li> <li><em>SkowAnnotations.PF.tsv</em> --> PF annotation of the Skow1.1 genome assembly</li> <li><em>SkowAnnotations.PP.tsv</em> --> PP annotation of the Skow1.1 genome assembly</li> </ul> <p>### Enrichment Data ###</p> <ul> <li><em>DESeqEnrichments.tsv</em> --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by DESeq2.</li> <li><em>MaSigProEnrichments.tsv</em> --> Pearson's chi-squared enrichment calculations for every annotation present in the Skow1.1 genome assembly for genes resolved as significantly differentially expressed by MaSigPro.</li> </ul>
HyUSPRe Report & Data on 'New experimental data on reactions between H2 and well cement and effects on fluid flow and mechanical properties of well cement
<p>In this study, new experimental data is presented of the effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties of oil well (class G) cement, relevant for underground hydrogen storage operations. Changes in mechanical properties (Young’s modulus, Poisson’s ratio and ultimate strength) have been analyzed using unconfined compressive strength (UCS) tests and confined cyclic loading tests on class G cement samples that were unreacted (cured for 3 days at 80°C) and exposed to lime-saturated brine and N<sub>2</sub> or H<sub>2</sub> for 1 and 2 months. Changes in cement mineralogy were analyzed by XRD analysis of the unreacted and exposed samples. The mechanical properties of elastic modulus and Poisson’s ratio are within the expected range of an oil well cement. Differences in Young’s modulus, Poisson’s ratio and ultimate strength are limited between unreacted, N<sub>2</sub>-exposed and H<sub>2</sub>-exposed samples, when comparing UCS tests or confined cyclic loading tests. Repeated UCS tests seem to indicate that the variation in Young’s modulus and ultimate strength increases after N<sub>2</sub> and H<sub>2</sub> exposure, but this observation needs to be confirmed in additional tests. During cyclic axial loading of confined cement samples, irreversible (plastic) deformation (compaction) occurs that affect static Young’s modulus. Also, effects of exceeding yield and failure strength on Young’s modulus are observed. Dynamic Young’s moduli and Poisson’s ratios derived from acoustic velocity measurements during confined cyclic tests show limited variation, in particular if static and dynamic Young’s modulus are compared. The mineralogical changes as identified using XRD analysis suggest minor changes between unexposed and H<sub>2</sub>- and N<sub>2</sub>-exposed samples, although XRD patterns indicate some minerals that could not be identified. The main conclusion is that effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties and mineralogical changes of class G cement is limited compared to unreacted or N<sub>2</sub> exposed samples for the investigated conditions. There is no indication that changes in mechanical properties of cement are such that cement integrity of wells used for underground hydrogen storage will be significantly affected. It should be emphasized that this conclusion is based on experiments on one type of cement (class G) and a limited set of conditions. In particular, additional tests to assess the reproducibility of current results and tests on samples that were exposed longer to H<sub>2</sub> and N<sub>2</sub> are of interest. Detailed effects of changing properties for the durability and integrity of wells can be derived by performing a parameter sensitivity analysis with well integrity modelling for the range in mechanical properties measured in this study.</p>
Catalytic Rules and Validation Results for "EzMechanism: An Automated Tool to Propose Catalytic Mechanisms of Enzyme Reactions"
<p>Dataset containing the "Rules of Enzyme Catalysis" as created during the development of EzMechanism and the validation results of the software. For more information see https://www.biorxiv.org/content/10.1101/2022.09.05.506575v1, and the M-CSA website in https://www.ebi.ac.uk/thornton-srv/m-csa/</p>
BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions
<p>Open data for "BCC-Cu nanoparticles: from a transient to a stable allotrope by tuning size and reaction conditions"</p>
Raw Data for the Article "Zwitterionic Halido Cyclopentadienone Iron Complexes and Their Catalytic Performance in Hydrogenation Reactions"
<p>This data set contains the raw data (NMR, ESI-MS, LC-MS, Elemental Analysis, VT-NMR) for the article "Zwitterionic Halido Cyclopentadienone Iron Complexes and Their Catalytic Performance in Hydrogenation Reactions" published in <em>Inorganic Chemistry</em>, DOI:</p> <p><a href="https://doi.org/10.1021/acs.inorgchem.2c04298">https://doi.org/10.1021/acs.inorgchem.2c04298</a></p> <p> </p>
RMG-DB-11: Enumerating Reaction Space for Small Molecule Chemistry
<p>This repository presents approximately 750 million atom-mapped reaction SMILES. Reactions are generated by applying templates from the Reaction Mechanism Generator (RMG) database to a subset of the species from GDB11. Thus, we refer to this dataset as RMG-DB-11 i.e., the Reaction Mechanism Generator Database whose species contain up to 11 heavy atoms. All SMILES have been canonicalized by RDKit. All reactions are labeled with their corresponding RMG template.</p> <p>This data serves as a crucial starting point for quantitative predictive chemistry. Many methods that search for transition state structures require atom-mapped SMILES, which this repository provides. This data is also well-suited for unsupervised pre-training of various machine learning models.</p> <p>To parse the data with Python, start with <em>import pandas as pd</em>. Reactions with 1-8 heavy atoms can be parsed using the following code snippet: <em>pd.read_csv(<filepath>)</em>. Reactions with 9 heavy atoms can be parsed using <em>pd.read_pickle(<filepath>, compression='zip')</em>. The file names below include the word "zip" as a helpful hint to use the compression argument. Due to the large number of reactions with 10 and 11 heavy atoms, these are split into smaller chunks. First untar the file using <em>tar -xvf <tar_archive></em> to obtain several zipped pickle files that can each be parsed using the same method as with 9 heavy atoms.</p>
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
<p>Dataset accompanying the paper: <em>"Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields"</em>. Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. <br> <br> The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways.</p> <p><strong>Published in NPJ Computational Materials</strong>: <a href="https://www.nature.com/articles/s41524-023-01124-2">https://www.nature.com/articles/s41524-023-01124-2</a><br> Formerly on Arxiv: <a href="https://arxiv.org/abs/2301.09931">https://arxiv.org/abs/2301.09931</a></p>
Study of the Crystallisation Reaction Behaviour to Obtain Struvite - Agronomic Potential of Struvite and Crystallisation results
<p>The potential of N and P recovering from digestate by means of its precipitation in the form of struvite is evident. However, it is necessary to optimise the process at a larger scale, to achieve results that can be extrapolated to evaluate the technical and economic feasibility of the process at an industrial scale. In this work, batch and pilot plant tests were carried out in order to consolidate, at a sufficiently relevant scale, the results obtained at lab scale. For this purpose, the parameters that have the greatest effect on the reaction yield in a fludised bed reactor were selected (Mg and P concentration, flow rate of the fluidising agent (air) and reaction time). Digestate produced in anaerobic digestion plant from pig manure was used as raw material. According to the results obtained, for the struvite crystallisation reaction, the great operational levels for the Mg/P, N/P, air flow rate and reaction time are 1.5, 4.0, 6.0 NL·min<sup>−1</sup> and 0.5 h, respectively. Finally, a study was carried out to establish the agronomic potential of the salt (struvite) as a biofertiliser in the turf crop, obtaining a similar behaviour of the struvite used in this work to that of commercial struvite.</p>
Optimized stationary points on the potential energy surface of the reaction of atomic oxygen O(3P) with acrylonitrile
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P) + acrylonitrile potential energy surface (PES).</p> <p>The PES has been published in our article “A Computational Analysis of the Reaction of Atomic Oxygen O(<sup>3</sup>P) with Acrylonitrile”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2021</strong>, 12958, 339-350), that can be found in https://doi.org/10.1007/978-3-030-87016-4_25 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D) + CH2CHCN and CN + CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D) + CH<sub>2</sub>CHCN (acrylonitrile) and CN + CH<sub>2</sub>CHCN.</p> <p>The PES has been published in our article “A Theoretical Investigation of the Reactions of N(<sup>2</sup>D) and CN with Acrylonitrile and Implications for the Prebiotic Chemistry of Titan”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 246-259), that can be found in https://doi.org/10.1007/978-3-031-10562-3_18 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D)+ C2H4 and N(2D)+ CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN.</p> <p>The PESs have been published in our article “Computational Investigation of the N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN Reactions: Benchmark Analysis and Implications for Titan’s Atmosphere”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2023</strong>, 14105, 705-717), that can be found in https://doi.org/10.1007/978-3-031-37108-0_45 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the S+(4S) + SiH2(1A1) and HSiS+/SiSH+ + NH3 reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of three reactions: S<sup>+</sup>(<sup>4</sup>S) + SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>), <sup>3</sup>HSiS<sup>+</sup> + NH<sub>3</sub> and <sup>3</sup>SiSH<sup>+</sup> + NH<sub>3</sub>.</p> <p>These PESs are part of our paper “The S<sup>+</sup>(<sup>4</sup>S)+SiH<sub>2</sub>(<sup>1</sup>A<sub>1</sub>) Reaction: Toward the Synthesis of Interstellar SiS”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 233-245), that can be downloaded in https://doi.org/10.1007/978-3-031-10562-3_17 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pV(T+d)Z level of theory.</p>
Optimized structures of selected stationary points on the potential energy surface of the HC3N + CN reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the HC<sub>3</sub>N + CN potential energy surface published in our article “Semiempirical Potential in Kinetics Calculations on the HC<sub>3</sub>N + CN Reaction” (<em>Molecules</em> <strong>2022</strong>, <em>27(7)</em>, 2297), that can be found in <a href="https://doi.org/10.3390/molecules27072297">https://doi.org/10.3390/molecules27072297</a> .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at M06-2X/6-311+G(d,p) level of theory.</p>
Modeling CH4 and CO2 cycling using porewater stable isotopes in a thermokarst bog in Interior Alaska: Results from three conceptual reaction networks
Quantifying rates of microbial carbon transformation in peatlands is essential for gaining mechanistic understanding of the factors that influence methane emissions from these systems, and for predicting how emissions will respond to climate change and other disturbances. In this study, we used porewater stable isotopes collected from both the edge and center of a thermokarst bog in Interior Alaska to estimate in situ microbial reaction rates. We expected that near the edge of the thaw feature, actively thawing permafrost and greater abundance of sedges would increase carbon, oxygen and nutrient availability, enabling faster microbial rates relative to the center of the thaw feature. (full abstract available in supplemental file 610_NeumannPorewaterExtendedMetadataText.pdf)
Supplementary Material for "Thermodynamic Reaction Control of Nucleoside Phosphorolysis"
<p>This is the supplementary material for our publication "Thermodynamic Reaction Control of Nucleoside Phosphorolysis".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Figure S1 and Tables S1-S3.</p> <p>The .zip file contains the raw data, metadata and results from Figure 1, 2, 4 and 5.</p> <p>The .xlsx file contains</p> <ol> <li>the apparent transformed values of the reaction enthalpy and entropy of nucleosides <strong>1</strong>-<strong>24</strong></li> <li>the calculated apparent Gibbs free energies of nucleosides <strong>1</strong>-<strong>24</strong> and</li> <li>the implementation of these values for the calculation of equilibrium conversions of nucleosides <strong>1</strong>-<strong>24</strong> with variable (adjustable) reaction conditions (concentrations of the nucleoside, phosphate and reaction temperature).</li> </ol> <p>For the software employed for spectra unmixing, please see doi: 10.5281/zenodo.3243376 and our previous work (doi: 10.3390/mps2030060) as well as its supporting material (doi: 10.5281/zenodo.3333469) for clarification.</p>
ScienceDex guides
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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.