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5,004 results for “Signature”
Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing
Open the record for dataset details and reuse information.
Unravelling the physiological and psychosocial signatures of pain by machine learning
<p>These datasets include information from 118 subjects, with 81 chronic pain patients across three different cohorts, Complex Regional Pain Syndrome (CRPS), Low Back Pain (LBP) and Spinal Cord Injury with Neuropathic Pain (SCI NP) and 37 healthy subjects. Each participant underwent 40 repetitions of experimentally induced pain, resulting in a total of 4,697 pain trials. Physiological signals (EDA and EEG) and psychosocial information have been recorded and collected. Age, gender, height, weight, BMI, medications, fatigue, sleep quality, perceived health, quality of life, sleep quality, and sick leave and validated questionnaires: Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Score (PCS), and Pain Self-Efficacy Questionnaire (PSEQ) and Multidimensional Assessment of Interoceptive Awareness (MAIA).</p> <p>This information has been used for the publication "Unravelling the physiological and psychosocial signatures of pain by machine learning".</p> <p><span><em><strong>Cite this dataset as:</strong></em></span><br>N. Gozzi, G. Preatoni, F. Ciotti, M. Hubli, P. Schweinhardt, A. Curt, S. Raspopovic, Unraveling the physiological and psychosocial signatures of pain by machine learning. Med 0 (2024). <a href="https://doi.org/10.1016/j.medj.2024.07.016" target="_blank" rel="noopener">10.1016/j.medj.2024.07.016</a></p> <p> </p> <p> </p>
Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
<p><strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong></p> <p>Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.</p> <p> </p> <p><strong>Change log for v1.0.1:</strong></p> <p>Fix wrong Bug ID for <em>sqlite</em> and add README clarification.</p> <p><strong>Change log for v1.0.2:</strong></p> <p>Added an example linking data in the repository to the table.</p>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
SignAture_Electricity_generation_data_compare_Latvia_2020_2022
<p>This dataset, related to the article 'Power System Modelling in the Baltic Countries: Data Accessibility and Consistency Aspects' (2023), compares electricity generation data for 2020 and 2022 from various sources in Latvia, providing both input and output values and associated metadata.</p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Luquillo LTER signature Monthly and Daily dataset
This dataset contains population, meteorological, phenological, and chemical data collected from tropical montane stream and forest ecosystems. This synthesis product merges 11 distinct datasets into a single comprehensive resource, facilitating easier comparison and study of diverse ecological data. Try our data explorer [here.](https://luqshiny.lter.network/sigds/) It provides a multi-faceted dataset essential to enhance biodiversity monitoring, ecosystem management, and global change research. Data spans from 1975 to 2024, with some gaps and missing data points. Particularly at the beginning of the time span measurements started with temperature and rainfall before other dataset collections began with the inception of the LUQ LTER in the late 1980s. For phenology 17 species were selected for inclusion, listed below. 11 dominant species as described in Uriarte et al. 2009 (DOI 10.1890/08-0707.1). As well as in decline species PALRIP- Palicourea riparia and CISVER - Cissus verticillata. Species that show a strong response to hurricanes PHYRIV - Phytolacca rivinoides- an understory herb and IPOTL -Ipomoea tiliacea- Morning glory vine. And a species thriving understory shrub SMIDOM - Smilax domingensis. - **From Uriarte et al. 2009 these 11 species represent 75% of the stems >=10 cm dbh in the LFDP plot: ** - **ALCLAT** - Alchornea latifolia - **CASARB** - Casearia arborea - **CECSCH** - Cecropia schreberiana - **DACEXC** - Dacryodes excelsa - **GUAGUI** - Guarea guidonia - **INGLAU** - Inga laurina - **MANBID** - Manilkara bidentata - **PREMON** - Prestoea montana - **SCHMOR** - Schefflera morototoni - **SLOBER** - Sloanea berteriana - **TABHET** - Tabebuia heterophylla - **PALRIP** - Palicourea riparia - For more details on methods and variables see each individual dataset: - Canopy Trimming Experiment (CTE) Litterfall: [https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-luq&identifier=162](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-lu
RPN-signature_Study2
Open the record for dataset details and reuse information.
Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site
<p>Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site"</p> <p>Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani </p> <p>Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication.</p> <p>Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load)</p> <p>Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
Flow_signatures_1366stations
16 flow signatures compiled using daily hydrograph time-series. The choice of flow signatures has been guided by a study by Olden and Poff (2003), which provides recommendations for selection of nine indices describing flow regimes with importance to hydro-ecology. In addition, five flow signatures commonly used in hydrology have been added for comparability (Qsp, CVQ, Q5, Q95, RBFlash) and two variables describing catchment response were calculated (RunoffCo and ActET). The selected flow signatures are: mean specific flow (Qsp), coefficient of variation of daily flow (CVQ), skewness of daily flow (skew), base flow index (BFI), 5th percentile (Q5), high flow discharge (HFD), 95th percentile (Q95), low flow frequency (Lowfr), coefficient of variability of high flow frequency (HighFrVar), coefficient of variability of low flow duration (LowDurVar), mean 30-days maximum (Mean30dMax), constancy of daily flow (const), coeficient of variability in annual number of reversals (RevVar), flashiness of flow (RBFlash), Runoff coefficient (RunoffCo), actual evapotranspiration (ActET). Only gauging stations with at least five whole calendar years of continuous daily data have been selected, and stations with obvious high regulation have been removed. No missing data was allowed over the period and the longest continuous time-series was used at each gauge. This means that time periods differ between gauging stations, the length in year of the used continuous timeseries and the starting year are given in the file (resp. nbYears and startYear). For definitions used for the different flow signatures see reference paper (Kuentz et al., 2016)." This data can be linked to the shapefile "flow_stations_sel1366" available as a separate download. To do so, link the "SUBID_EHYP" filed of the shapefile with the "SUBID" field of this dataset.
Characterising evapotranspiration signatures for improved behavioural insights
<p>The dataset provides actual evapotranspiration (AET) data extracted at three temporal scales from eddy covariance flux towers and two remotely sensed AET products, namely MOD16A2GFv06.1 and CMRSET, across 17 Fluxnet sites in Australia. The study that utilized this dataset is currently under review in the journal of <em>Hydrology and Earth System Sciences</em>, and the preprint is available at https://doi.org/10.5194/hess-2024-373</p>
Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)
<p>Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (<em>Brassica napus</em>)</p> <p><strong>Abstract</strong></p> <ul> <li>Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.</li> <li>Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with <em>Brassica napus</em> constitutive QDR to four contrasting fungal pathogens: <em>Alternaria brassicicola</em>, <em>Botrytis cinerea</em>, <em>Pyrenopeziza</em><em> brassicae</em> and <em>Verticillium longisporum. </em>We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to <em>V. longisporum </em>and potentially broad-spectrum QDR.</li> <li>This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in <em>B. napus </em>and is a significant resource for the scientific community. </li> </ul> <p><strong>Description of data files</strong></p> <p><strong>Full dataset for input into AT analysis </strong>Full datasets (infection phenotypes for <em>A. brassicicola, B. cinerea, </em>or <em>V.longisporum, </em>ROS measurements for chitin, flg22, or elf18) and link to original <em>P. brassicae </em>dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, <a href="https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075">https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075</a>). </p> <p><strong>Table S1 </strong>Mean, normalized phenotype data for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis.<strong> </strong></p> <p><strong>Table S2 </strong>Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.</p> <p><strong>Table S3</strong> Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum</em>) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). </p> <p><strong>Table S4 </strong>184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and<em> Verticillium longisporum</em>) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S5</strong> Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. </p> <p><strong>Table S6 R</strong>esults from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module, GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively. The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module, GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.</p> <p><strong>Table S7 </strong>Shared gene expression markers (GEMs) associated with resistance to different pathogens (<em>Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae </em>and <em>Verticillium longisporum</em>). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p><strong>Table S8 </strong>List of genes in linkage disequilibrium with the top marker for <em>Verticillium longisporum</em> resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in <em>Brassica napus</em> reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the <em>B. napus</em> pantranscriptome is indicated. Predicted <em>Arabidopsis thaliana</em> orthologs and corresponding descriptions are shown where possible. </p> <p> </p>
Assessing evapotranspiration realism in rainfall-runoff models using evapotranspiration signatures
<p> </p> <p>This dataset contains simulated actual evapotranspiration (AET) data derived from five conceptual hydrological models and input data applied to 14 catchments in Australia. The data spans the period from 1980 to 2022. The five models included in this dataset are:</p> <ul> <li>SIMHYD</li> <li>IHACRES</li> <li>VIC</li> <li>SACRAMENTO</li> <li>GR4J</li> </ul> <p>These models were implemented in version 2.1 of the MaRRMoT framework.</p> <p>Here, the models were calibrated using two different approaches:</p> <ol> <li>Calibration based on discharge data only. (Folder: ModelCalQ_Data)</li> <li>Calibration using a composite objective function that incorporates both discharge and AET data. (Folder: ModelCalQnAET_Data)</li> </ol> <p>Example script is also included in each model folder, such as ‘<em>Run_Simhyd_MaRRMoT_Cal_Spartan.m’</em>, to facilitate the running of the models and understanding of the calibration process.</p> <p> </p>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</p>
MeV SIMS analysis of irradiation effects on molecular signatures
<p>Characterizing the effect of MeV ion beam irradiation on biological tissues is important for proton beam therapy, which is routinely used as a form of cancer treatment. It is also important for optimizing protocols for multimodal elemental and molecular imaging. Elemental mapping of trace elements in tissues has been carried out for a long time using nuclear microprobe analysis. However, the effect of MeV ion beams on biological samples is largely unexplored. These effects have been explored in Surrey using two mass spectrometry imaging (MSI) techniques – matrix-assisted laser desorption electrospray (MALDI) and desorption electrospray ionization (DESI). The combination of these techniques with ion beam analysis (IBA) presents a few challenges, namely substrate compatibility and de-localization of elemental markers during measurements. As such, MeV-secondary ion mass spectrometry (SIMS) is being explored as an alternative technique for molecular imaging of biological tissues. MeV SIMS, unlike conventional keV SIMS, allows the detection of intact molecules, making it a prime candidate for the molecular analysis of biological samples. This presents an opportunity to benchmark the capabilities of MeV SIMS against established and widely used techniques such as DESI and MALDI. Experiments carried out at Surrey (reported at the ICNMTA 2020) observed that proton beam-induced damage could be mitigated through the application of a MALDI matrix (employed in MALDI as an ionization aid and sample protection). Thus, the role of this matrix is explored in MeV SIMS experiments.</p>
Data files: Single-cell RNA profiling of Plasmodium vivax-infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets
<p>Scripts, preprocessed count matrices, and single-cell data objects generated in <strong>“Single-cell RNA profiling of <em>Plasmodium vivax</em><em>-</em>infected hepatocytes reveals parasite- and host- specific transcriptomic signatures and therapeutic targets” </strong></p>
Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres - Posteriors
<p>Supplementary material for 'Signatures of Nitrogen Chemistry in Hot Jupiter Atmospheres', ApJL, 2017.</p> <p>Contains the posterior probability distributions resulting from atmospheric retrievals of WASP-31b, WASP-63b, and HD 209458b.</p>
Dataset: Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs
<p>Dataset used in the statistical analysis of the publication "Sex differences in the impact of social relationships on individual vocal signatures in grey mouse lemurs (<em>Microcebus murinus</em>)"</p> <p><strong>Abstract</strong></p> <p>Vocali<span>z</span>ations coordinate social interactions between conspecifics by conveying information concerning the individual or group identity of the sender. Social accommodation is a form of vocal learning where social affinity is signalled by converging or diverging vocali<span>z</span>ations to those of conspecifics. To investigate whether social accommodation is linked to the social lifestyle of the sender, we investigated sex-specific differences in social accommodation in a dispersed living primate, the grey mouse lemur, where females form stable sleeping groups whereas males live solitarily. We used 482 trill calls of 36 individuals from our captive breeding colony to compare acoustic dissimilarity between individuals with genetic relatedness, social contact time and body weight. Our results showed that female trills become more similar the more time females spen<span>d</span> with each other independent of genetic relationship, suggesting vocal convergence. In contrast, male trills were affected more by genetic than social factors. However, focus<span>s</span>ing only on sociali<span>z</span>ed males, male trills diverged from each other the more time males were cage partners. Thus, grey mouse lemurs show the capacity for social accommodation, with females converging their trills to signal social closeness to sleeping group partners, whereas males do not adapt or diverge their trills to signal individual distinctiveness. </p> <p> </p> <p>For details concerning the recording of the trills confer to the publication at doi: 10.1098/rstb.2023.0193</p>
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</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.