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Dataset results
311 results for “function dataset”
Dataset used for the submitted manuscript "The hump-shaped effect of plant functional diversity on the biological control of a multi-species pest community"
<p>Dataset</p>
DrosophilaMuscleFunction: Dataset for Drosophila Muscle Function Studies
<p>This is the data and code at the time of publication of Hatfield et al. in Scientific Reports.</p>
[Dataset] enhanced functional connectivity properties of human brains during in-situ nature experience
<p>Raw data</p> <p>Artifact-free EEG raw data recorded by Emotiv Epoc (sample rate 128/s, 14 channels).</p>
Dataset of Measured Binaural Room Transfer Functions of Individuals
<p>A dataset of BRIRs from 25 individuals is presented. The BRIRs are recorded in a listening lab (ITU-BS.1116-1) and an empty seminar (V = 182 m³, T60 = 2.0 s). Additionally, for some persons the BRIRs from an acoustic enhanced seminar room and a small lecture hall are included. The recorded source directions with a distance of 2.2 m are 0°, 30°, 60°, 180°, 240°, 300° or all 12 hours positions for the persons with the two additional rooms. The used dataset is stored in the SOFA (Spatially Oriented Format for Acoustics) file format.</p>
Dataset for the manuscript 'Orbital-optimized Density Functional Calculations of Molecular Rydberg Excited States with Real Space Grid Representation and Self-Interaction Correction'
<p>Dataset for the manuscript 'Orbital-optimized Density Functional Calculations of Molecular Rydberg Excited States with Real Space Grid Representation and Self-Interaction Correction'</p>
Dataset of laterality of functional smiles
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Dataset for the manuscript "Homogeneous microenvironmental conditions under nurses promote facilitation". FUNCTIONAL ECOLOGY
<p>The following directory contains the data necessary to replicate the results obtained in the manuscript entitled '<strong>Homogeneous Microenvironmental Conditions Under Nurses Promote Facilitation</strong>'</p><p>We provided two spreadsheets: 'BD_beneficiaries.xlsx' and 'BD_nurses.xlsx.'</p><p>'BDbeneficiaries.xlsx' contains the data needed to identify beneficiary/non-beneficiary species (sheet: network_<i>data) and the data of functional traits for the tested species (sheet: Beneficiary-Non</i>_beneficiary).</p><p>'BD<i>nurses.xlsx' contains the data needed to identify nurses/non-nurses species (sheet: network_data) and the data of environmental modification provided by nurse/non-nurse species (sheet: nurse-non</i>_nurse effects).</p><p>Both archives contain a metadata sheet describing the meaning of each variable.</p>
Divergent evolution of sleep functions - Joyce et al 2024 Dataset - Part 3 of 4
<p>This is the full experimental dataset associated to "Divergent evolution of sleep functions" by Joyce et al 2024, Nature Communications. See https://lab.gilest.ro/papers/divergent-evolution-of-sleep-functions/ for more information.</p> <p>The dataset contains 86 zip files for a total of about 170GB. Once uncompressed, they will explode 622 ethoscope db files for a total of 330Gb, covering behavioural analysis of more than 11.000 animals. These are the RAW data as collected from the ethoscopes. A separate zip archive with all the R/Python scripts and the metadata is also provided. This also contains confocal images for BRP analysis.</p> <ul> <li>Part 1 of 4: <a href="https://doi.org/10.5281/zenodo.10554851" target="_blank" rel="noopener">10.5281/zenodo.10554851 </a></li> <li>Part 2 of 4: <a href="https://doi.org/10.5281/zenodo.10557238" target="_blank" rel="noopener">10.5281/zenodo.10557238</a></li> <li>Part 3 of 4: <a href="https://doi.org/10.5281/zenodo.10557310" target="_blank" rel="noopener">10.5281/zenodo.10557310 (this page)</a></li> <li>Part 4 of 4: <a href="https://doi.org/10.5281/zenodo.10966461" target="_blank" rel="noopener">10.5281/zenodo.10966461</a></li> </ul>
Training dataset for label-free microfluidic droplet classification in single cell functional assays
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Benchmark dataset for protein function prediction that integrates various protein information
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
Dataset: Drone-Related Transfer Function
<p>This dataset is the supplementary .mat files for the journal "Drone Audition: On Measurements and Modeling of Drone-Related Transfer Functions" accepted to IEEE Transactions on Audio, Speech and Language Processing 2024.</p> <p>Link: https://ieeexplore.ieee.org/abstract/document/10971256</p> <p>Measurement_Grid.mat: The measurement grid of the experimental setup</p> <p>DRTF_Meausrements.mat: The processed IR measurements</p>
Unprocessed PROSAIL-generated datasets of plant functional traits with associated spectra
Open the record for dataset details and reuse information.
Divergent evolution of sleep functions - Joyce et al 2024 Dataset - Part 4 of 4
<p>This is the full experimental dataset associated to "Divergent evolution of sleep functions" by Joyce et al 2024, Nature Communications.</p> <p>All the data were collected in the Gilestro laboratory at Imperial College London</p> <p>See https://lab.gilest.ro/papers/divergent-evolution-of-sleep-functions/ for more information.</p> <p>The dataset contains 86 zip files for a total of about 170GB. Once uncompressed, they will expose 622 ethoscope db files for a total of 330Gb, covering behavioural analysis of more than 11.000 flies. These are the RAW data as collected from the ethoscopes. A separate zip archive with all the R/Python scripts and the metadata is also provided. This also contains confocal images for BRP analysis.</p> <ul> <li>Part 1 of 4: <a href="https://doi.org/10.5281/zenodo.10554851" target="_blank" rel="noopener">10.5281/zenodo.10554851 </a></li> <li>Part 2 of 4: <a href="https://doi.org/10.5281/zenodo.10557238" target="_blank" rel="noopener">10.5281/zenodo.10557238</a></li> <li>Part 3 of 4: <a href="https://doi.org/10.5281/zenodo.10557310" target="_blank" rel="noopener">10.5281/zenodo.10557310</a></li> <li>Part 4 of 4: 10.5281/zenodo.10966461 (this page)</li> </ul>
Dataset for: Relative contribution of high and low elevation soil microbes and nematodes to ecosystem functioning
<ol> <li><span>Ecosystem productivity is largely dependent on soil nutrient cycling which, in turn, is driven by decomposition rates governed by locally-adapted belowground microbial and invertebrate communities. How climate change will impact soil biota and the correlated ecosystem functioning, however, remains largely an open question.</span></li> <li><span>To address this challenge, we first characterized the functional identity of soil microbial and nematode communities originated from the foothills or in the sub-alpine soils of the Alps, and then, using a full-factorial reciprocal transplant common garden experiment at two elevations, we asked whether soil biota from low elevation were more prone in generating nutrient cycling than high elevation soil biota. Specifically, we separately transplanted soil microbial and nematode to community from low and high elevation in their home or opposite elevation in pots added with a common plant community. </span></li> <li><span>We found evidence for ecotypic and functional differentiation of the microbial and nematode communities growing in. We also observed a decrease in microbial diversity and activity at high elevation, and additionally, through nematodes' functional characterization, we found increased fungal-dominated energy channels at high elevation. </span></li> <li><span>Moreover, while we found little effect of soil biodiversity change based on elevation of origin on plant productivity, soils inoculated with microbes originating from low elevation respired more than those originating at high elevation. This observation correlates well with the observed faster carbon degradation rates by the low-elevation microbial communities. </span></li> <li><span>Climate change can reshuffle soil invertebrate communities depending on organism-specific variation in range expansion, ultimately affecting soil fertility and vegetation productivity.</span></li> </ol>
ARFM(Adaptive receiver function seismic migration): codes and dataset for application in SE Tibetan plateau
<p>We develop a post-stack adaptive receiver function (RF) seismic migration method to image subsurface disconitnuities. Contents of numerical experiments are in the compressed file 'program_synthetic_experiments.tar'. RF dataset and codes for the application of the adaptive receiver function migration method in southeastern Tibetan plateau are in other compressed files.</p>
Patch-clamp dataset (SyncroPatch384PE) for control variants used in a calibrated functional patch clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome
<p><span>Modern sequencing technologies have revolutionised our detection of gene variants. However, in most genes, including <em>KCNH2</em>, the majority of missense variants are currently classified as variants of uncertain significance (VUS). The aim of this study </span><span class="normaltextrun"><span>was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in the <em>KCNH2 </em>gene. </span></span><span>The assay was designed according to recommendations proposed by the ClinGen Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variant on current density and channel gating in order to determine the sensitive and specificity of the assay. All 17 pathogenic variant controls had reduced current density and 13/14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying moderate or supporting evidence strength for VUS reclassification. Inclusion of <em>KCNH2</em> functional assay evidence enabled us to reclassify 6 out of 44 VUS as likely pathogenic. The high-throughput patch clamp assay can provide moderate strength evidence for clinical interpretation of clinical <em>KCNH2</em> variants and </span><span>demonstrates the value of developing automated patch clamp assays for functional characterisation of ion channel gene variants.</span></p>
Dataset for deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
<p>Dataset files of atomic structures and Hamiltonian matrices of graphene, MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Please note that the DFT results in this dataset were calculated using OpenMX. This means that if you want to use a DeepH model trained on this dataset to calculate properties, you need to use the <a href="https://github.com/mzjb/overlap-only-OpenMX">overlap calculated using OpenMX</a>. The orbital information required for overlap calculations can be found in the <a href="https://www.nature.com/articles/s43588-022-00265-6">paper</a>.</p>
Dataset (MATLAB format) from Yang et al (2022) Thalamus-driven functional populations in frontal cortex support decision-making. Nat. Neurosci.
<p><strong>Summary</strong></p> <p>These experiments measure neuronal responses from the left hemisphere of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. In a subset of the recordings, we inactivate activity of one of the brain regions providing inputs to ALM (ipsilateral S1/S2, contralateral ALM, and ipsilateral Thal<sub>ALM</sub>) in some trials.</p> <p>This dataset contains data from 9626 single units, 73 mice, 347 sessions. The dataset is described as “the primary dataset” in the paper below. The experiments (including experiment methods) are described in the paper.</p> <p><em>Yang W, Tipparaju SL, Chen G, Li N, (2022). Thalamus-driven functional populations in frontal cortex activity supports decision-making. Nat Neurosci, in press.</em></p> <p> </p> <p>The second dataset used in the paper can be downloaded also from Zenodo at</p> <pre><a href="https://doi.org/10.5281/zenodo.6713616">https://doi.org/10.5281/zenodo.6713616</a></pre> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Li N (2022); Data and simulations related to: Thalamus-driven functional populations in frontal cortex activity supports decision-making. Yang et al (2022) Nat Neurosci.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6846161">http://dx.doi.org/10.5281/zenodo.6846161</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>func</strong>”</p> <p>2) unzip "<strong>scripts</strong>"</p> <p>3) Run any scripts "<strong>demo_*.m</strong>" within "<strong>scripts</strong>"</p> <p> </p> <p>A collection of analyses scripts that reproduce figures in " Yang et al (2022)" is included in the folder "<strong>\scripts\</strong>"</p> <p><strong>demo_compute_activity_modes_independentTrials.m</strong> – plot t-SNE embedding and all the response profile clusters; plots PSTHs from an example cluster; plots neurons connected to S1/S2, cALM, ThalALM on the t-SNE.</p> <p><strong>demo_compute_tSNE_embedding.m</strong> – t-SNE embedding of neuronal response profiles (based on PSTH shape)</p> <p><strong>demo_compute_activity_modes.m</strong> – compute activity modes from neuronal population responses.</p> <p><strong>demo_compute_selectivity_vector_stability.m</strong> – analysis of selectivity vectors</p> <p> </p> <p> </p>
Dataset for the manuscript: Phylogenetic and functional constraints of plant facilitation rewiring. ECOLOGY
<p>The following directory contains the data necessary to replicate the results obtained in the manuscript entitled: <strong>Phylogenetic and functional constraints of plant facilitation rewiring</strong></p> <p>We provided two spreadsheets containing the data "Adult species data" and "Interactions data.xlsx". Each archive contains a readme textbox with detailed information on the variables included in the archives</p>
ReSpaN - Spanish Dataset for non-functional requirements classification
<p>The ReSpaN (Spanish Dataset for non-functional requirements classification) dataset is conformed by requirements collected from final degree projects from one University. Manual labeling was performed by 7 annotators in such a way that each requirement had at least three labels. The labels were the categories and subcategories of the ISO/IEC 25010 quality model. The definitions of each category and subcategory are available at https://iso25000.com/index.php/normas-iso-25000/iso-25010, in both Spanish and English. The label ’No agreement’ was used for requirements with no majority in the labeling process. The final classification of each requirement is based on unanimity or majority. This dataset was created following the FAIR principles. </p> <p>Cited as:</p> <p>Maria-Isabel Limaylla-Lunarejo, Nelly Condori-Fernandez, and Miguel R. Luaces. 2023. Towards a FAIR Dataset for non-functional requirements. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23). Association for Computing Machinery, New York, NY, USA, 1414–1421. https://doi.org/10.1145/3555776.3578611</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.