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107 results for “automated analysis”
The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study
<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social – non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map. ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset. The findings support that the “social brain” is highly sensitive to the mere presence of social information. </p>
Research data supporting "Single particle automated raman trapping analysis"
<p>Research raw data supporting the publication:</p> <p>Penders J., et al., Nature Communications. (2018) 9:4256 | DOI: 10.1038/s41467-018-06397</p>
Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"
<p>This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks.</p> <p>The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset.</p> <p>The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points.</p> <p>The annotations are given in two types of files:</p> <p>"TRL.csv" files: contain the image names and corresponding TRL values (mm).</p> <p>"pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.</p>
Data for "Automated analysis of surface facets: the example of cesium telluride"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Automated analysis of surface facets: the example of cesium telluride".</p> <p>The file "Cs2Te_surfaces_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.</p>
Data from: Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
Open the record for dataset details and reuse information.
LUNAR: Automated input generation and analysis for reactive LAMMPS simulations input and output files
Open the record for dataset details and reuse information.
Automated analysis of HPLC chromatograms obtained during the dehydration of N-Acetylglucosamine into 3-Acetamido-5-acetylfurane using Python
<p><strong>Content: </strong>This dataset contains High Performance Liquid Chromatography (HPLC) chromatograms, obtained while studying the dehydration of N-Acetylglucosamine into 3-Acetamido-5-acetylfurane in DMAc, DMF and NMP employing various catalysts and screening different reaction conditions. Reaction conditions can be retrieved from the metadata attached. Importantly, a python script to automatically extract, plot and analyze the HPLC-data obtained is provided.</p><p><strong>Acknowledgements</strong>: The authors acknowledge support by the German Research Foundation (DFG) within NFDI4Cat (ID 441926934). Parts of this work were funded by the Cluster of Excellence Fuel Science Center (EXC 2186, ID: 390919832) funded by the Excellence Initiative by the German federal and state governments. Furthermore, the authors thank Jens Heller and Frederic Thilmany for performing the HPLC measurements.</p><p> </p>
Automated bio-AFM generation of large mechanome data set and their analysis by machine learning to classify prostatic cell lines_Training base 100 PC3-GFP
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Data for "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics"
<p>This repository contains a representative subset of filter paper images used to evaluate the various experimental conditions in the manuscript "Laundry to Laboratory: Automated Image Analysis for the Characterization of Fibrous Microplastics." </p>
msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis
<p>This record contains example and result data of msiFlow.</p> <p>msiFlow is a collection of automated workflows for reproducible and scalable multimodal mass spectrometry imaging (MSI) and immunofluorescence microscopy (IFM) data processing and analysis. Using an experimental mouse model for urinary tract infection, induced by uropathogenic E.coli (UPEC), we generated data by</p> <ul> <li>matrix-assisted laser desorption ionisation mass spectrometry imaging with laser-induced postionisation (MALDI-2 MSI) using the Bruker timsTOFfleX instrument</li> <li>transmission-mode MALDI-2 MSI (t-MALDI-2)</li> <li>immunofluorescence microscopy (IFM) using the MACSima system from Miltenyi </li> </ul> <p>msiFlow was tested on MALDI-2 MSI, t-MALDI-2 MSI and IFM data of control and UPEC-infected mouse bladder sections. In IFM we used Ly6G and actin for staining neutrophils and the muscle layer. We validated msiFlow on MALDI MSI data of bone marrow (BM)-derived neutrophils. Tentative lipid annotations were validated by MALDI DDA MSI and MALDI MS/MS. All data used and results generated by msiFlow are included in this dataset (besides the intermediate results of the MALDI-2 preprocessing due to data size).</p> <p>The dataset contains the following zip files:</p> <table> <tbody> <tr> <td><strong>zip file</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ly6g_heterogeneity.zip</td> <td>example and result data (Ly6G clusters) for molecular_heterogeneity_flow</td> </tr> <tr> <td>if_segmentation.zip</td> <td>example and result data (Ly6G segmentation) for if_segmentation_flow</td> </tr> <tr> <td>ly6g_heterogeneity_signatures.zip</td> <td>example and result data (lipids for Ly6G clusters) for molecular_signatures_flow</td> </tr> <tr> <td>ly6g_molecular_signatures.zip</td> <td>example and result data (lipids for Ly6G) for molecular_signatures_flow</td> </tr> <tr> <td>msi_if_registration.zip</td> <td>example and result data for msi_if_registration_flow</td> </tr> <tr> <td>msi_segmentation.zip</td> <td>example and result data (segmented MSI bladder data) for msi_segmentation_flow</td> </tr> <tr> <td>region_group_analysis.zip</td> <td>example and result data (regulated lipids in different bladder tissue regions) for region_group_analysis_flow</td> </tr> <tr> <td>macsima.zip</td> <td>raw IFM data of UPEC-infected bladders containing Ly6G, actin and autofluorescence images</td> </tr> <tr> <td>maldi-bm-neutrophils.zip</td> <td>raw and pre-processed MALDI MSI data of BM-derived neutrophils</td> </tr> <tr> <td>t-maldi-2.zip</td> <td>raw t-MALDI-2 MSI data of a UPEC-infected bladder section</td> </tr> <tr> <td>maldi-2-<em>group-sampleno</em>.zip</td> <td>raw MALDI-2 MSI data of a control/UPEC bladder section</td> </tr> <tr> <td>MALDI_DDA_MSI.zip</td> <td>raw MALDI MSI data acquired in DDA mode</td> </tr> <tr> <td>TIMS_MS_MS.zip</td> <td>raw MALDI TIMS MS/MS data</td> </tr> </tbody> </table> <p> </p>
PoreScript: Semi-automated Pore Size Analysis Algorithm Data Set
<p>This data set contains files related to the PoreScript semi-automatic pore size image analysis algorithm. The three MATLAB files needed for the PoreScript algorithm are named the following: </p> <p>(<a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_RelativeIntensityFinder_no_crop.m">Jenkins_RelativeIntensityFinder_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_UserInterface_no_crop.m">Jenkins_UserInterface_no_crop.m</a>, <a href="https://zenodo.org/api/files/47f5a723-b2de-41b6-baed-f1a6335c4b84/Jenkins_PoreSizeCalculator_no_crop.m">Jenkins_PoreSizeCalculator_no_crop.m</a>).</p> <p>Access the latest version of the program here:<a href="https://github.com/djenkins95/PoreScript_Update_9_26_23"> <strong>https://github.com/djenkins95/PoreScript_Update_9_26_23</strong></a></p> <p>Updated MATLAB files are more accessible to a wider range of SEM software. The updated version asks for the known length of your scale bar in pixels. There are many ways to measure the length of your scale bar. I recommend using the free software FIJI. Use the *Straight* (drawing tool to trace your scale bar), then click Analyze > Measure to determine the length in pixels. It should be noted that the length in pixels will be the same for any image taken on the same instrument, at the same magnification, and saved as the same file type (e.g., .tiff), so you can reference the length in future data sets without needed to remeasure the scale bar.</p> <p>The Zenodo repository includes the unanalyzed SEM images, analyzed images, raw pore size data, analyzed pore size data, and older .m versions.</p>
Nanopore MinION Run Metrics and genomic DNA fragment size analysis data from automated phenol-chloroform extractions (RBI LabDroid Maholo)
<p>Nanopore MinION run MinKNOW statistical metrics output, Agilent Femto Pulse and Tape Station gDNA fragment size analysis reports of genomic DNA isolated from automated RBI LabDroid Maholo organic extractions.</p>
VesselExpress: Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs
<p>This dataset contains raw, segmented and skeletonized 3D light-sheet microscopic image volumes of blood vessels of different organs which were processed by VesselExpress. Please find the software here: https://github.com/RUB-Bioinf/VesselExpress. For details on how to run and setup the software please watch our tutorial (https://youtu.be/a8GWVKJNh68).</p>
Dataset of the research article "A semi-automated analysis of displacement-to-length scaling of the grabens affecting lunar Floor-Fractured craters"
<p>This dataset includes all the raster data (DEM, orthoimage, slope) and vectors (shapefiles) used in our research, investigating the relationships between displacement and length of the faults affecting Lunar Floor-Fractured craters.</p>
Research Data Supporting "Coupling Lipid Nanoparticle Structure and Automated Single Particle Composition Analysis to Design Phospholipase Responsive Nanocarriers"
<p>Raw research data supporting Barriga, Pence, et al. 2022, Advanced Materials. <a href="https://doi.org/10.1002/adma.202200839">https://doi.org/10.1002/adma.202200839</a></p>
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Supporting material for "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test"
<p>The data were uploaded to support the manuscript "Pharmacological validation of individual animal locomotion, temperature and behavioural analysis in group-housed rats using a novel automated home cage analysis system: a comparison with the modified Irwin test" for the submission to Journal of Pharmacological and Toxicological Methods.</p>
Interactive Outputs for "Automated Mixture Analysis via Structural Evaluation"
<p>HTML files with interactive outputs related to the mixture assignments discussed in the paper "Automated Mixture Analysis via Structural Evaluation."</p>
Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines - Artefacts DataSet
<p>This Archive contains the Artefact of the paper, submitted at GPCE2021 :<br> <a href="https://doi.org/10.1145/3486609.3487208">Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines</a></p> <p> </p> <p>It contains the dataset and the results of our Feature Location techniques when applied to this dataset.</p>
Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"
<p>This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)</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.