Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
53
datasets available to search
ShareScore release 0.7.1
Dataset results
53 results for “Valence”
Valence processing differs across stimulus modalities (Multi-echo)
Open the record for dataset details and reuse information.
fMRI: Audiovisual Valence Congruence
Open the record for dataset details and reuse information.
Coding table for chapter 30 'Valency change and causation' in Bowern (ed. 2023)
<p>This is the coding table used for chapter 30 'Valency change and causation' (pp. 344-359) in the <i>Oxford Guide on Australian Languages </i>(Bowern, ed. 2023).</p>
IT-VaLex: Index Thomisticus Valency Lexicon
<p>IT-VaLex: <em>Index Thomisticus</em> Valency Lexicon. Website: <a href="https://itreebank.marginalia.it/itvalex/">https://itreebank.marginalia.it/itvalex/</a></p>
Visual Dictionary of Tibetan Verb Valency: Data
<p>This repository contains a JSON version of the data powering the <a href="https://bit.ly/VisualDictionary-TibetanValency">Visual Dictionary of Tibetan Verb Valency</a> together with its documentation. The structure of the data is explained in the documentation section on 'Dictionary data'.</p> <p>The Visual Dictionary of Tibetan Verb Valency was produced as part of the UKRI-funded project <a href="https://gtr.ukri.org/projects?ref=AH%2FP004644%2F1">Lexicography in Motion a History of the Tibetan Verb</a> (LIM) at SOAS.</p>
Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions
<p>These datasets display the raw data for the manuscript: Huanhuan Zhou, Philipp Groppe, Thomas Zimmermann, Susanne Wintzheimer, Karl Mandel, Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions, Journal of Colloid and Interface Science, Volume 658,<br>2024, Pages 199-208, https://doi.org/10.1016/j.jcis.2023.12.051.</p> <p>The data connection file serves as an explanation for all datasets and their connection to the data displayed in the manuscript.</p>
CLDF dataset derived from Hartmann et al.'s "Valency Patterns Leipzig" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hartmann, Iren & Haspelmath, Martin & Taylor, Bradley (eds.) 2013. Valency Patterns Leipzig. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://valpal.info)</p> </blockquote>
Supplementary material: Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium
<p>The ridge plots attached here accompany the supplement to the manuscript <em>Negative valence in Obsessive-Compulsive Disorder: A worldwide mega-analysis of task-based functional neuroimaging data of the ENIGMA-OCD consortium</em> (Dzinalija et al., 2024)<em>. </em>These are full results of Figures 1, 2C, and 3C of the manuscript and Figures S3, S5, S7 and S9 of the supplement depicting whole-brain Bayesian multilevel models run using the Regional Bayesian Analysis toolbox (RBA; Chen et al., 2019). Whole-brain analyses were parcelated into the Schaefer-Yeo 7-network 200-parcel cortical atlas (Schaefer et al., 2018) and the Melbourne 32-region subcortical atlas (Tian et al., 2020). Results are presented according to contrast of interests: [Negative > Neutral], [OCD > Neutral], [Threat > Neutral], and [OCD > Threat], and effects of interest: [Diagnosis = OCD or HC], [MED = medication], [AO = age of onset], [YBOCS = OCD severity], and [Intercept = task effect]. </p> <p>P+ values denote the probability that there is increased brain activation in a given region of the Schaefer 200-parcel 7-network cortical atlas and Melbourne 32-region subcortical atlas. We used the guidelines proposed by Chen et al. (2019) to infer credibility of evidence, namely taking a positive posterior probability (P+) of <0.10 or >0.90 as indication of moderate evidence and, <0.05 or >0.95 or <0.025 or >0.975 as strong or very strong evidence, respectively. To interpret the pairwise comparisons presented as Group1-vs-Group2, posterior distributions to the right of the green no-effect line represent regions in which individuals in Group 1 show credible evidence for higher activation than individuals in Group 2. Regions with posterior distributions to the left of this line show credible evidence for higher activity in Group 2 than in Group 1. (Darker) red color represents regions in which individuals in Group 1 show moderate-to-very-strong evidence for higher activation than Group 2. (Darker) blue color represents regions in which Group 2 show moderate-to-very-strong evidence for higher activation than Group 1. Grey color represents regions in which there is no strong evidence of a difference between Group 1 and Group 2. Schaefer-Yeo 200-parcel atlas name abbreviations can be retrieved <a href="https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal/Parcellations">via this link.</a></p>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
Dataset of photoemission valence-band mapping and band reconstruction of 2H-WSe2
<p>Photoemission band mapping, band structure calculation, and reconstructed band dispersion of layered semiconductor 2<em>H</em>-WSe<sub>2</sub>, a type of 2D transition metal dichalcogenide.</p> <p><strong>Provenance</strong>: The experimental data were measured using the SPECS METIS 1000 3D detector with extreme UV pulses centered at 21.7 eV as the photoemission light source. The samples were commercially purchased and directly used in the experiment.</p> <p>The calculated data were converted and preprocessed from an existing record (http://dx.doi.org/10.17172/NOMAD/2020.03.28-1), which contains the band structure of 2<em>H</em>-WSe<sub>2</sub> calculated using density functional theory (DFT) at the level of LDA, PBE, PBEsol, and HSE06 exchange-correlation functionals using FHI-aims (version 171221_1).</p> <p><strong>Content</strong>: The data contain binned and preprocessed photoemission data; Preprocessed DFT calculations used for initializing the band reconstruction; Postprocessed band reconstruction data (including 14 identifiable valence bands).</p> <p><strong>Usage</strong>: The dataset may be used for reproducing the results in (https://arxiv.org/abs/2005.10210), computational benchmarks or separately for other materials science applications. For reconstruction, see the source code and examples on GitHub (https://github.com/mpes-kit/fuller).</p>
Data for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et al.
<p>Data and python scripts for 'Probing resonating valence bonds on a programmable germanium quantum simulator' by Chien-An Wang et <em>al</em>.</p> <p>Corresponding authors Corentin Déprez (C.C.Deprez@tudelft.nl) // Menno Veldhorst (M.Veldhorst@tudelft.nl)</p>
Valence and salience encoding in the central amygdala
Open the record for dataset details and reuse information.
Mogul geometry analysis of valence angles in CSD entry BEXYIO
<p>Results of a Mogul geometry analysis of the valence angles in Cambridge Structural Database entry BEXYIO.</p>
The valence-dominance model applies to body perception
<p>First impressions of a person are often based on appearance. The widely accepted valence-dominance face perception model (1) posits that social judgements of faces fall along two orthogonal dimensions: trustworthiness (valence) and dominance. The current study aimed to establish principal components of social judgements based on perception of bodies, hypothesising that these would follow the same dimensions as face perception. Stimuli were black and white photographs showing bodies dressed in grey clothing, standing in their natural posture (left profile). Raters (N=237) judged the stimuli on 14 traits used in Oosterhof and Todorov's original study (1). Data were analysed using principal components (PCA) analysis, as in the original study, with an additional exploratory factor analysis using oblique rotation. While PCA analysis produced a third dimension in line with several replications of the original study, results from the exploratory factor analysis produced two dimensions, representing trustworthiness and dominance, providing support for the hypothesis that social perceptions of bodies can be summarised using the valence-dominance model. These two factors could represent universal perceptions we have about people. Future research could explore social judgements of humans based on other stimuli, such as voices to evaluate whether trustworthiness and dominance dimensions are consistent across modalities.</p>
Aymer de Valence Tomb Detail
Located in Westminster Abbey, London. Died: 1324. https://www.westminster-abbey.org/abbey-commemorations/commemorations/william-and-aymer-de-valence 33 photos taken in June 2021 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
1p_BLA_sync_permutation_social_valence
<div> <h1>Genetically- and spatially-defined basolateral amygdala neurons control food consumption and social interaction</h1> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#genetically--and-spatially-defined-basolateral-amygdala-neurons-control-food-consumption-and-social-interaction"></a></div> <p>Highlights:</p> <ol> <li>Classification of molecularly-defined glutamatergic neuron types in mouse BLA with distinct spatial expression patterns.</li> <li>BLALypd1 neurons are positive-valence neurons innately responding to food and promoting normal feeding.</li> <li>BLAEtv1 neurons innately respond to aversive and social stimuli.</li> <li>BLAEtv1 neurons promote fear learning and social interactions.</li> </ol> <p> </p> <div> <h2>"Step by step" for codes for BLA neuron calcium imaging analysis</h2> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#step-by-step-for-codes-for-bla-neuron-calcium-imaging-analysis"></a></div> <ol> <li>Either using our demo files or your calcium imaging and behavior CSV files need to be used: Demo lists: 1. Figure4_food, 2. Figure5_social, 3. Figure5_fear_conditioning, 4. suppl.fig10_longitudinal_footshock_social.</li> </ol> <div> <p>Note</p> <p>Demo files are already preprocessed H5 files, so you can skip 2-3steps.</p> </div> <ol> <li>you need to download "core_codes" <strong>(logger.py, preprocessing.py, util.py)</strong> to synchronize the extracted behavior statistics with calcium traces (The TTL emission-reception delay is negligible (less than 30ms), therefore the behavioral statistics time series can be synchronized with calcium traces by the emission/receival time on both devices) and it would generate combined one H5 file (behavior+calcium data)</li> <li>Run <strong>Synchrnoize_h5generation.py</strong> code to apply core-codes (preprocessing) to your data:</li> </ol> <div> <pre><code>import core.preprocessing as prep </code></pre> <div> </div> </div> <ol> <li>You can run each code (5 codes) described in the paper to generate the results: <strong>1. Fear Conditioning, 2.social, 3. food, 4. permutation, 5, suppl.10 social_footshock.py</strong> code number 4 is the percentage comparison with shuffling of data in Figure4-5 as bar graph you can reproduce using the code: Figure4_5_permutation_bargraph.</li> </ol> <div> <h3>required</h3> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#required"></a></div> <div> <p>Note</p> <p>-System requirements Python (3.10.8): we used a Python IDE for professional developers by JetBrains, Pycharm. Packages: suite2p : <a href="https://github.com/MouseLand/suite2p">https://github.com/MouseLand/suite2p</a></p> </div> <div> <pre><code>pip install git+https://github.com/MouseLand/suite2p.git </code></pre> <div> </div> </div> <blockquote> <p>matplotlib</p> </blockquote> <div> <pre><code>pip install matplotlib pip install PyQt5 </code></pre> <div> </div> </div> <blockquote> <p>numpy</p> </blockquote> <div> <p>Warning</p> <p>our code included : matplotlib Qt5Agg An Error is happening because Google Colab and Jupyter run on virtual environments which do not support GUI outputs as you cannot open new windows through a browser. Running it locally on a code editor(Spyder, or even IDLE) ensures that it can open a new window for the GUI to initialize.</p> </div> <div> <p>Tip</p> <ul> <li>"our analysis pipeline is based on basic python packages:"</li> </ul> </div> <div> <pre><code>> import numpy as np >import h5py as h5 >import pandas as pd </code></pre> <div> </div> </div> <p>Installation guide above</p> <ul> <li>Demo -Demo_data.zip</li> </ul> <div> <h3>Acknowledgement</h3> <a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence#acknowledgement"></a></div> <p>Yue Zhang</p> <p> </p> <p>https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence</p> <blockquote> <p><a href="https://github.com/limserenahansol/1p_BLA_sync_permutation_social_valence">Github link for this </a></p> </blockquote>
Newspaper valence datasets for van der Veen & Bleich (2024)
<p>Replication datasets for average valence calculations (full newspapers and national representative corpora).</p>
Data for: Representation of rewards differing in their hedonic valence in the caudate nucleus correlates with the performance in a problem-solving task in dogs (Canis familiaris)
<p>Abstract</p> <p>We have investigated dogs’ (<em>Canis familiaris</em>) abilities in associating different sounds with food rewards of different incentive value. The establishment of the association was tested in a problem-solving behavioural paradigm, as well as in an fMRI study on the same subjects (N=20). The aim was to show behavioural, as well as parallel neural effects of the association formation between the two sounds and two different associated food rewards.<br> The latency of solving the problem was considered as an indicator of motivational state. In our behaviour study we found that dogs were quicker in solving a problem upon hearing the sound associated with food higher in reward value, suggesting that they have successfully associated the sounds with the corresponding food value. In the fMRI study, the cerebral response to the two sounds was compared both before and after the associative training. Two bilateral regions of interest were explored: the caudate nucleus and the amygdala. After the associative training the response in the caudate nucleus was higher to the sound related to a higher reward value food than to the sound related to a lower reward value food, which difference was not present before the associative training. We found an increase in the amygdala response to both sounds after the training. In a whole-brain representational similarity analysis, we found that cerebral patterns in the caudate nucleus to the two sounds were different only after the training. Moreover, we found a positive correlation between the dissimilarity index in the caudate nucleus for activation responses to the two sounds and the difference in latencies to solve the behavioural task: the quicker the dog solved the behavioural task the greater the difference in the neural representation of the two sounds was. In summary, family dogs’ brain activation patterns reflected their expectations based on what they learned about the relationship between two sounds and their associated rewards.</p> <p>This dataset contains</p> <ul> <li>Raw data (four functional runs n = 20)</li> <li>Dog brain template</li> <li>ROI results (Caudate nucleus and amygdala percentage of BOLD signal change and caudate nucleus response to sounds during the two post-training runs adding or not the Inter-scan interval as a covariate in the GLM model n = 20,)</li> <li>RSA results ( Dissimilarity change between sounds (post-training > pre-training) and dissimilarity index per participant n = 20)</li> </ul>
Strong pressure dependence of the valence band maximum in tetragonal ZrO2
<p>This dataset will serve the publication of a work on</p> <p><strong>Strong pressure dependence of the valence band maximum in tetragonal ZrO2</strong></p> <p>The archive only includes the resulting files from the workflow leading to such work. the analysis tools can be found at:</p> <p>https://github.com/mdforti/ZrO2-Notebookos-Newpot</p> <p>The file tree is somewhat complicated but we think it will be clear. several folders are available, <code>ZrO2-$factor</code> where <code>factor</code> is the scale factor applied to the prototype unit cell.</p> <p>Inside each folder, you will find several subfolders, each of them for the different parts of the calculation.</p> <ol> <li> <p><code>RLX2dir</code> corresponding to the relaxation at constant volume.</p> </li> <li> <p><code>TOTEN_5dir</code> has the calculation of the DOS at high resolution, and the vaspruns and EIGENVALS there are used for BS energy isosurfaces construction.</p> </li> <li> <p><code>BANDSdir</code> saves the calculation for the complete band structure for a standard k-path.</p> </li> <li> <p><code>ABANDSdir</code>, <code>ZBANDSdir</code>, <code>MBANDSdir</code>, <code>TBANDSdir</code> and <code>NSMBANDSdir</code> save the results for the individual k-paths around each of the VBM candidates.</p> </li> </ol> <p> </p> <p>We kindly acknowledge the partial support of ANPCyT - Argentina through grants PICT-2018-01671 and PICT-2018-02366.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.