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1,294 results for “reactions”
DFT optimised structure used for the paper "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst"
<p>DFT optimised structures used to calculate the OER activities in "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst". The structures are bundled in two databases, LiIrO3.db which contains all structures for alpha-LiIrO<sub>3</sub> and KLiIrO3-disordered.db which contains all the structures for the disordered Li<sub>0.75</sub>K<sub>0.25</sub>(H<sub>2</sub>O)<sub>0.50</sub>IrO<sub>3 </sub>structure. The structures can be retrieved using the Atomic Simulation Environment (ASE, https://wiki.fysik.dtu.dk/ase/index.html). The keywords 'ads' and 'surface' can be used to search the structure, e.g. surface='Z-step' and ads='*OOH' will give the structure with OOH adsorbed on the Z-step surface (see paper for details on the different surfaces).</p>
Raw Data to "Density functional theory study of CO formation through reactions of polycyclic aromatic hydrocarbons with atomic oxygen (O(3P))"
<p>This data is a supplement to the publication <a href="https://doi.org/10.1016/j.fuel.2018.12.047">https://doi.org/10.1016/j.fuel.2018.12.047</a>. The data includes Turbomole input and output files. The calculations are performed using DFT/TPSSh-D3/TZVP method and Turbomole version 7.2. The equilibrium structures for the reactions of polyaromatics are named as following:<br> C<sub>X</sub>H<sub>Y</sub> (<strong>S1</strong>) + O -> C<sub>X</sub>H<sub>Y</sub>O (<strong>S2</strong>) -> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) + H (i) O addition and H abstraction<br> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) [-> <strong>S4</strong> -> <strong>S5</strong> ] -> CX-1HY-1 (<strong>S6</strong>) + CO (ii) Single, two, or three step CO elimination</p> <p>The transition state structures are named according to the naming of the corresponding reactant and product. For example, the transition state connecting the structure S3 to S5 is named as T35. Under some of the transition state directories, intrinsic reaction coordinate calculation output can be found under the directories named as "IRC".<br> <br> </p> <p><br> The LibreOffice Calc spreadsheet "SUPPINFO.ods" includes the activation and reaction energies to the reaction steps.</p>
Impact of processing and storage conditions on color stability of strawberry puree: the role of PPO reactions revisited
<p>The effect of pre-heating fresh strawberries (hot break) and the use of refrigerated temperatures prior to pasteurization on the stability of anthocyanins, vitamin C, color and polyphenol oxidase (PPO) activity during storage (42 days at 35 ºC) of strawberry puree was studied. Hot break resulted in 20% residual PPO activity and caused 10% anthocyanin degradation, whereas vitamin C was unaffected. After mashing, purees were stored at 4 ºC and 25 ºC for 3 hours. During this period, anthocyanins and PPO activity remained constant independently of the processing history but ascorbic acid was oxidized faster at 25 ºC. Pasteurization caused complete inactivation of PPO, reduction of anthocyanins (25%), of <em>a*</em> value (6%) and of vitamin C (50%). Neither partial inactivation of PPO early in the processing (hot break) nor the use of refrigeration prior to pasteurization had a positive effect on color and anthocyanin stability of strawberry puree during subsequent storage, suggesting that PPO-derived reaction products formed during processing have a very limited impact on color degradation during shelf-life.</p>
Examples of ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model
<p>Examples of atomic layer deposition ALD saturation profiles in rectangular channel LHAR structures simulated with a diffusion-reaction model by Ylilammi et al. (Journal of Applied Physics <strong>123</strong>, 205301 (2018); <a href="https://doi.org/10.1063/1.5028178">https://doi.org/10.1063/1.5028178</a>) as function of (a) Reactant A pulse time <em>t</em>, (b) Reactant A partial pressure <em>p</em>, and (c) sticking coefficient <em>c</em>. Parameters used in the simulation, if not otherwise stated: channel height <em>H</em> 500 nm, temperature 250°C, 250 cycles, inert carrier gas partial pressure 500 Pa, Reactant A molar mass 100 g/mol, inert carrier gas molar mass 28 g/mol, Reactant A diameter 0.600 nm, inert gas diameter 0.374 nm, adsorption capacity 4 metal atoms per nm<sup>2</sup>, density of the material grown 3.5 g/cm<sup>3</sup>, pulse time 0.1 s, Reactant A partial pressure 100 Pa, sticking coefficient 0.01. </p> <p>Abbreviations: ALD = atomic layer deposition, LHAR = lateral high aspect ratio</p>
X-ray diffraction images for 5-aminolevulinic acid dehydratase with a putative reaction intermediate resembling the product porphobilinogen bound.
<p>X-ray diffraction images for yeast 5-aminolevulinic acid dehydratase co-crystallised with the substrate 5-aminolevulinic acid. The structure demonstrated a putative product-like intermediate bound covalently to Lys 263 with an amino side chain ligated to the active-site zinc ion in a position normally occupied by a catalytic hydroxide ion. The data were collected in two passes using the ESRF beamline ID29 in Feb 2002 and extend to approximately 1.6 Å resolution. </p>
Dataset for "A QM-CAMD approach to solvent design for optimal reaction rates"
<p>Dataset accompanying "A QM-CAMD approach to solvent design for optimal reaction rates". </p> <p>All Gaussian09 files for Step 2 of the proposed QM-CAMD algorithm</p> <p>Sample GAMS file for Step 4</p> <p>Excel spreadsheet containing a summary of results for all 3 case studies</p>
Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism
<ul> <li>Eight versions of COBRA-compliant SBML files are available for each Recon 2M.1 and Recon 2M.2 depending on the use of: MNXref versus BiGG IDs; Entrez gene IDs (GPR associations) versus Ensembl transcript IDs versus RefSeq transcript IDs versus UCSC transcript IDs (TPR associations for the last three database IDs).</li> <li>892 personal GEMs (only T-GEMs) built with Recon 2.2</li> <li>1,784 personal GEMs (both P-GEMs and T-GEMs) built with Recon 2M.1</li> <li>892 personal GEMs (only T-GEMs) built with Recon 2M.2</li> </ul> <p> </p> <p><strong>Publication</strong></p> <p>Jae Yong Ryu<sup>1</sup>, Hyun Uk Kim<sup>1</sup> & Sang Yup Lee<sup>*</sup>. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism., <em>Proc. Natl. Acad. Sci. U.S.A.</em>, 2017, http://www.pnas.org/content/early/2017/10/23/1713050114</p> <p> </p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
Dataset for project Lipobodies, related to the development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction
<p>This dataset contains primary (including raw data) that supports the results of the design and development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction</p>
Expanding the chemical space using a Chemical Reaction Knowledge Graph
<p>This contains:</p><ul><li>the reaction graph dataset used to train the link prediction model</li></ul><p>Homepage: https://github.com/MolecularAI/reaction-graph-link-prediction</p>
Data collection for Tsuji et al., 2023, Anoxygenic phototrophic Chloroflexota member uses a Type I reaction center
<p>Supplementary data files associated with Tsuji et al., 2023, "Anoxygenic phototrophic <i>Chloroflexota</i> member uses a Type I reaction center". These files are used by code in a corresponding GitHub repository (<a href="https://github.com/jmtsuji/Ca-Chlorohelix-allophototropha-RCI">https://github.com/jmtsuji/Ca-Chlorohelix-allophototropha-RCI</a>) that shows how various analyses that are presented in the paper were conducted.</p><p>Files included:</p><ul><li>HPLC-based spectroscopy data ("...-HPLC-run1.tsv.gz" or "...-HPLC-run2.tsv.gz") -- hyper-spectral data files, generated by a diode array detector, that are associated with pigment analyses in the paper. See the corresponding Github repo for how these files are analyzed.</li><li>Supplementary data about "<i>Ca. </i>Chloroheliales"-associated RCI:<ul><li>I_TASSER_homology_models_full_output.tar.gz -- Gzipped tarball containing the full output from I-TASSER for homology models of key phototrophy-related genes encoded by '<i>Candidatus </i>Chlorohelix allophototropha' and '<i>Candidatus </i>Chloroheliales bin L227-5C'. After unpacking the tarball, view a summary of the I-TASSER output for each gene by clicking on the 'index.html' file in that gene's folder.</li></ul></li><li>Boreal Shield lake survey data:<ul><li>lake_survey_MAGs.tar.gz -- Gzipped tarball containing the full collection of 756 metagenome-assembled genomes (MAGs) recovered from the Boreal Shield lake survey, corresponding to those mentioned in Supplementary Data 3. The FastA nucleotide genome sequences, FastA nucleotide predicted protein-coding gene sequences, FastA amino acid predicted protein sequences, and Genome Flat Files (GFFs) for all genomes are provided in the fna, ffn, faa, and gff subdirectories, respectively.</li><li>lake_survey_MAGs_eggnog_annotations.tar.gz -- Gzipped tarball containing annotations (produced via EggNOG) for all predicted proteins among the 756 MAGs recovered from lake metagenome data. Because proteins were pre-clustered prior to annotation, a "orf2gene" file inside the tarball maps the gene clusters to the ORF IDs used for each genome.</li><li>lake_survey_MAGs_featureCounts.tsv.gz -- GZipped tab-separated table containing the mapping statistics of metatranscriptome reads on all protein-coding genes from the 756 MAGs recovered from lake metagenome data.</li><li>lake_survey_Ca_Chloroheliales_MAGs_info.tar.gz -- A subset of information from the previous three files specific to genome bins ELA319 and ELA729, which represent RCI-encoding "<i>Ca</i>. Chloroheliales" members.</li></ul></li><li>Intermediate files involved in some of the genome assembly work in this paper:<ul><li>Capt_S15_sequencer_data_raw.tar.gz -- Gzipped tarball containing the raw Illumina MiSeq output data for the '<i>Candidatus </i>Chlorohelix allophototropha' subculture 15 sequencing run. The run represents a read cloud sequencing run relying on TELL-Seq technology. Indices can be parsed directly from raw output data using the Tell-Read pipeline.</li><li>scaffold.full.fasta.gz -- the assembled scaffolds generated using Tell-Read and Tell-Link on the above raw MiSeq output data.</li><li>Ca_Chloroheliaceae_bin_L227_5C_prokka_ORFs.faa.gz -- predicted open reading frames (ORFs) from the curated genome of '<i>Candidatus </i>Chloroheliales bin L227-5C'. These ORFs were predicted using prokka and were used for some of the analyses presented in the paper. Most analyses used the annotations available on NCBI (generated by PGAP) for this strain.</li></ul></li></ul>
Strong uptake of gas-phase organic peroxy radical (ROO•) by solid surfaces driven by redox reactions
<p>This repository contains publicly available data supporting the article *Strong uptake of gas-phase organic peroxy radical (ROO•) by solid surfaces driven by redox reactions*, Durif et al. (2024)</p>
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
<p>This repository contains all the data and code associated with the manuscript "Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients"</p> <p>Mass spec and absorption chromatogram data are in reaction_set_1.zip, reaction_set_2.zip, and simulated_reaction_set.zip. The chemprop model trained on the Deep4Chem dataset is in Deep4Chem_chemprop.zip.</p>
Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis
<p>Measurement data, simulation data and code from the manuscript Braccini et al. "Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis" </p> <p>ArXiv preprint arXiv:2309.11270 [nucl-ex] at https://doi.org/10.48550/arXiv.2309.1127</p>
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
Dataset for "From CO2 to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-Alloyed GaInSn Catalyst"
<p>Experimental raw data for the article "<em>From CO<sub>2</sub> to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-alloyed GaInSn Catalyst</em>", published in <em>Journal of Physical Chemistry C </em>(2024). DOI:10.1021/acs.jpcc.4c05482.</p> <p>The data set is organized according to the publication's figures. XPS data given here is the raw data without binding energy calibration. For the manuscript, binding energies in a series of samples were calibrated by taking the strongest peak, where no chemical shift was to be expected, as a reference.</p>
Supplementary material for "In-flight reactions of nocturnally migrating birds to winds"
<p><strong>Abstract</strong></p> <p>Available knowledge on in-flight reactions of nocturnal bird migrants to winds is reviewed, with emphasis on the challenging topographical and meteorological conditions in Western Europe, and differences from the situation in North America discussed. Conclusions drawn are used for a new approach: using individual radar tracks of nocturnal migrants (mainly passerines) as well as winds measured at their flight altitudes, we defined the basic direction (BD=average flight direction of all migrants tracked under negligible wind influence) as a reference. For two altitudinal zones above a radar site near Nuremberg, we modelled the deviations of tracks and headings from BD for increasing wind from six 60° sectors. A comparison of birds’ air speeds Va with winds from four 90°-sectors confirmed that Va increased with opposing winds from ~11 to 13 (14) m/s; a similar increase occurred with side winds. An expected, slight decrease of Va with increasing following winds was only indicated for high-flying, not for low-flying birds. A predicted increase in average Va due to decreasing air density with increasing height was not observed; possible explanations (birds climbing to high altitudes in following, but not in strong opposing winds) are discussed. Over the whole autumn migration season, headings were concentrated in a sector of ±30° around 230° in both altitudinal zones. Prevailing winds from 230 to 320° (SW–NW, i.e. opposing from right) led to widely scattered tracks primarily between 190° and 270°, but additional ones in the SE sector (mainly 100°–170°). The analysis of tracks and headings relative to BD revealed the following features. (1) Overcompensation was frequently observed at low wind speeds (<3 m/s); (2) under all wind conditions, but particularly with opposing winds and at low flight levels, tracks were widely scattered, including birds deviating more than 90° from BD. (3) Under opposing and side winds from the right compensatory efforts led to partial drift compensation up to wind speeds of ~8–10 m/s. Because efforts to compensate drift dwindled with increasing wind speeds, birds were fully drifted. Many even shifted their heading to due south and, hence, overdrifted. (4) Opposing and side winds from the left induced partial compensation at low flight levels and full drift above 1500 m asl. (5) The lateral components of the rare and weak following winds led to tracks close to expected minimal drift (without important compensation needed). In general, migrants compensated less for deviations by wind force than expected. The tendency of birds to maintain headings close to BD under opposing winds was so strong that many individuals continued migration with minimal progress over ground or even with retrograde migration as an extreme. On the other hand, there was an omnipresent fraction of birds with tracks far from seasonally favourable directions, including reverse migration.</p>
USPTO-LLM: A Large Language Model-Assisted Information-enriched Chemical Reaction Dataset
<p>USPTO-LLM is an <strong>information-enriched chemical reaction dataset</strong> that provides more side information (reaction conditions and reaction steps division) for developing new reaction prediction and retrosynthesis methods and inspires new problems, such as reaction condition prediction. It comprises over <strong>247K chemical reactions</strong> extracted from the patent documents of USPTO (United States Patent and Trademark Office), encompassing abundant information on reaction conditions. </p> <p>We employ large language models to expedite the data collection procedures automatically with a reliable quality control process. The extracted chemical reactions are organized as <strong>heterogeneous directed graphs</strong>, allowing us to formulate a series of prediction tasks, such as reaction prediction, retrosynthesis, and reaction condition prediction, in a unified graph-filling framework.</p>
REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robotic Failures and Subsequent Robotic Explanations.
<p>REFLEX Dataset is a comprehensive collection of multimodal Human Behavioral reactions to Robot Failures and Explanations. <br><br>The version 1.0 is a representative sample of this dataset with the reactions from 5 users out of a total 55 users.</p> <p>This version 1.1.0 is the full dataset with the reactions from a total 55 users.<br><br>Please refer to the Readme in the zipped file for further information.</p> <p><br>This data was recorded from a user study and has been processed for anonymization.</p> <h2>About Data</h2> <p>This description gives a detailed process on how the data was collected. It should describe the conditions under which the data was recorded and also the devices used to record the data.</p> <h3>Data Organisation</h3> <p>The data is structured by strategy and participant, as shown below:</p> <pre><code>Strategy Dir/ -Participant Dir/ - analysis - questonnaire - facetorch - openface - gaze - hume - body - voice - time - video_cam1 - video_cam2 </code></pre> <p>We employed five different strategies (C1, C2, C3, D1, D2), collecting data from 11 participants for each strategy. The data for each participant is organized within a corresponding folder.</p> <p>Participants are labeled based on their assigned strategy. For example, data from the first participant under the “Fixed Low” (C1) strategy can be found in the C1-1 subfolder within the C1 directory.</p> <h3>Collected Data</h3> <p>Each participant folder contains various datasets related to different modalities. All visual data are collected using the camera 1 video. The collected data are outlined below:</p> <ul> <li> <p><strong>Anonymized Videos</strong> (<code>video_cam1.mp4</code>, <code>video_cam2.mp4</code>) - Visual Representation:</p> <ul> <li>Video from camera 1 (robot side of view)</li> <li>Video from camera 2 (experiment side of view)</li> </ul> </li> <li> <p><strong>Analysis</strong> (<code>analysis.csv</code>) - Failure Instance Description:</p> <ul> <li>Failure type</li> <li>Explanation strategy</li> <li>Explanation level</li> <li>Phase (Pre, Failure, Explanation, Resolution)</li> <li>Start/End frame and time of failure</li> <li>Task Resolved</li> </ul> </li> <li> <p><strong>Questionnaire</strong> (<code>questionnaire.csv</code>) - Failure Instance Description:</p> <ul> <li>Participant Data (Age, Gender, etc)</li> <li>Answers of explanation-satisfaction rate question for rounds and overall experiment</li> </ul> </li> <li> <p><strong>Facetorch</strong> (<code>facetorch.csv</code>) - <a href="https://github.com/tomas-gajarsky/facetorch" target="_blank" rel="nofollow noopener">Facetorch</a> - Face:</p> <ul> <li>Arousal/Valence levels</li> <li>Presence of Facial Action Units (AUs)</li> <li>Dominant Emotion (Out of six basic emotions and neutral)</li> </ul> </li> <li> <p><strong>OpenFace</strong> (<code>openface.csv</code>) - <a href="https://github.com/TadasBaltrusaitis/OpenFace" target="_blank" rel="nofollow noopener">OpenFace</a> - Face, Gaze, Head:</p> <ul> <li>Eye Gaze (2D and 3D Landmarks)</li> <li>Eye Direction (vector and in radians)</li> <li>Head Pose Estimation (Pose Estimation, Rotation)</li> <li>Face Landmarks (2D and 3D Landmarks)</li> <li>Facial Action Units (0.0-1.0 intensity scores, occurrences)</li> </ul> </li> <li> <p><strong>Gaze</strong> (<code>gaze.csv</code>) - Gaze:</p> <ul> <li>Eye Gaze Classification (e.g., Robot, Task, Miscellaneous)</li> </ul> </li> <li> <p><strong>Hume</strong> (<code>hume.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Face:</p> <ul> <li>48 Emotion likelihoods</li> <li>Facial Action Units (0.0-1.0 score)</li> <li>Facial Descriptions (0.0-1.0 score)</li> </ul> </li> <li> <p><strong>Voice</strong> (<code>speech.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Speech:</p> <ul> <li>Speech conversation data</li> <li>Emotional likelihoods inferred from prosody</li> </ul> </li> <li> <p><strong>Body</strong> (<code>body.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a> - Body:</p> <ul> <li>Pose classifications (e.g., crossed arms, arms behind back)</li> <li>2D and 3D Pose Landmarks</li> </ul> </li> <li> <p><strong>Time</strong> (<code>time.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a>:</p> <ul> <li>Associated timestamp and time for each frame of camera 1 video.</li> </ul> </li> </ul> <p>Notes</p> <ul> <li>Data was synchronized based on the `video_cam1.mp4`</li> <li>The `hume.csv` and `gaze.csv` files contain data only for frames within failure periods.</li> <li>Failure events were divided into four phases:<br> 1. Pre-failure phase: Period before the failure occurs<br> 2. Failure phase: When the actual failure action takes place<br> 3. Explanation phase: When the robot provides an explanation for the failure<br> 4. Resolution phase: When the robot guides the participant to resolve the issue</li> </ul> <h2>How to Visualize Participant Data</h2> <p>Please visit the github repository: https://github.com/andreasnaoum/reflex-viz</p>
Metadata of "Ethylenediammine is not detrimental to the photoactivity of the bacterial photosynthetic reaction center"
<p>Metadata of "Ethylenediammine is not detrimental to the photoactivity of the bacterial photosynthetic reaction center"</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.