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1,582 results for “manuscript”
Additional movies for a manuscript titled 'Leaf epidermal cells respond to application and removal of mechanical force by distinct cytosolic calcium waves'
<p>This repository provides 21 additional videos that highlight the spectrum of mechanically stimulated calcium responses observed in the leaf epidermis of <em>Arabidopsis t</em>. expressing the genetically encoded calcium indicator R-GECO1. These movies correspond to figures in a manuscript entitled 'Leaf epidermal cells respond to application and removal of mechanical force by distinct cytosolic calcium waves'. In each movie, a cantilever exerts approximately 5 mN force to the epidermis (where the cantilever looks like a shadow in the movies). Each movie contains a scale bar and the relative time (hr:min:sec). Placement of the cantilever generally occurs after approximately 1 minute.</p>
Data for NHESS manuscript by Biass et al. (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data
<p>This repository contains the data produced in the context of the following paper:</p> <blockquote> <p>Biass S, Jenkins SF, Aberhard WH, Delmelle P, Wilson T (2022): Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data, Accepted in NHESS</p> </blockquote> <p>Naming convention is: `run_date`_`landcover`_`impact_metrics`_`VI`_`anomaly`_test.pkl, where:</p> <ul> <li>Landcover is either crops, shrubs, herbaceous vegetation (grass), forests (trees) or all together</li> <li>Impact metrics is either minV (impact magnitude) or minT (impact duration)</li> <li>VI is the vegetation index (here, EVI)</li> <li>Anomaly is the impact indicator (here, cumulative difference index)</li> </ul> <p>Refer to the associated paper for more information on the methodology.</p> <p>Files are saved as .pkl and were generated by the <a href="https://explainerdashboard.readthedocs.io/en/latest/">explainerdashboard</a> library. They are the result of <a href="https://xgboost.readthedocs.io/en/stable/">XGBoost</a> runs that were optimised with <a href="https://optuna.org">Optuna</a> and analysed with the <a href="https://shap.readthedocs.io/en/latest/">SHAP</a> library. They contain:</p> <ol> <li>The explanatory variables and observed and computed target variables for all features</li> <li>The SHAP values</li> </ol> <p>To load the files, use <a href="https://explainerdashboard.readthedocs.io/en/latest/cli.html?highlight=load#explainerdashboard.explainers.BaseExplainer.from_file">this method</a>.</p> <p> </p>
dRFEtools manuscript supplementary data
<p>This contains the supplementary data for the <a href="https://github.com/LieberInstitute/dRFEtools_manuscript">dRFEtools_manuscript</a>.</p>
Dataset for manuscript Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano
<p>This archive file contains datafiles used in "Precursor-free eruption triggered by edifice rupture at Nyiragongo volcano" manuscript. </p> <p>i.e. : Ash Index, Lava flow shp, fractures shp, eruptive fissure shp, GNSS, InSAR, Seismic catalog, SO2 measurmement and Dike Modeling inputs and results.</p>
Data and scripts for the manuscript of svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data
<p>This upload include data and scripts supporting the results described in the manuscript of <em>svaRetro and svaNUMT: modular packages for annotating retrotransposed transcripts and nuclear integration of mitochondrial DNA in genome sequencing data</em><em>. </em>Detailed description of the contents can be found in README.txt.</p>
Data associated with manuscript titled "Landscape predictors of Human-Leopard conflicts in and around Annapurna Conservation Area."
<p>The data set belongs to the research conducted in Annapurna Conservation Area regarding Human Leopard Conflict (HLC). </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>
HTR - Araucania - XIX manuscript
<p><strong>General</strong></p> <p>Ground Truth dataset for Spanish 19th typewritten OCR (XML-ALTO)</p> <p>The archives come from the events of the Occupation of Araucania (1850-1881) in Chile. They are archived in the 'Colección manuscritos' of the Archivo Central Andres Bello - Universidad de Chile.</p> <p>Thereby, it is not possible to publicly distribute the images (.jpg)</p> <p>To use them for segmentation/recognition model training, please contact me : archivo.central@uchile.cl</p> <p> </p> <p><strong>Methodology</strong></p> <p>Transcription rules :</p> <p>- xxx for blurred or unreadable characters<br> - ^+letters for superscript letters<br> - ⁋ for new paragraph</p> <p>Using the Kraken OCR engine in finetuning with the Menu_MacFrench template. A template uses the NFKD method.</p> <p>Segmonto ontology</p> <p> </p> <p><strong>Evaluation</strong></p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Quantity (GT)</strong></td> <td> <table> <thead> <tr> <th> </th> <th><strong>Val_acc</strong></th> </tr> </thead> </table> </td> <td><strong>Test_acc</strong></td> <td><strong>CER</strong></td> <td><strong>WER</strong></td> </tr> <tr> <td><strong>HTR-Araucania_XIX</strong></td> <td>180</td> <td> <table> <tbody> <tr> <td>0,90354</td> </tr> </tbody> </table> </td> <td> <table> <tbody> <tr> <td> </td> <td>0,8673</td> </tr> </tbody> </table> </td> <td>0,05598</td> <td>0.21423</td> </tr> <tr> <td><strong>HTR-Araucania_XIX_NFKD</strong></td> <td>180</td> <td>0,89872</td> <td>0,8563</td> <td> <table> <tbody> <tr> <td> </td> <td>0,06646</td> </tr> </tbody> </table> </td> <td>0.24963</td> </tr> </tbody> </table> <p><strong>Others</strong></p> <p>JSONL file for NER annotation in <code>ner/</code> (MISC, LOC, PERS, ORG, DATE)</p>
Dataset for manuscript "Laboratory investigation of hydraulic fracture growth in Zimbabwe gabbro"
<p>Dataset for manuscript: "Laboratory investigation of hydraulic fracture growth in Zimbabwe gabbro" -- dataset of GABB-003, GABB-005 and GABB-006 experiments</p>
Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/?id=7186602">open entry</a>.</p>
Figures for manuscript of Guillem-Amat et al, Insects
<p>Here I upload two files containing the figures and the supplementary figures attached to the manuscruipt entitled "Immunodetection of truncated forms of the α6 subunit of the nAChR in the brain of spinosad resistant <em>Ceratitis capitata</em> phenotype", submitted by authors Guillem-Amat et al. to Insects. </p>
Data set associated to simulations performed within the manuscript "Endosperm turgor pressure both promotes and restricts seed growth and size".
<p>This dataset contains the following files</p> <ul> <li>2F4-Col0-iku2-Fig.3f and Supp. Fig.9 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose unesterified pectins are labelled with the 2F4 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>JIM5-Col0-ap2-Fig. 4d and Supp Fig. 12b <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the JIM5 antibody (channel 2)</li> <li>2 genotypes: Col0 and ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>JIM5-Col0-iku2-Fig. 3h and Supp Fig. 10 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the JIM5 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>LM19-Col0-ap2-Fig. 4b and Supp Fig. 12a <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the LM19 antibody (channel 2)</li> <li>2 genotypes: Col0 and ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>LM19-Col0-iku2-Fig. 3d and Supp Fig. 8 <ul> <li>Slices of seeds from 3 to 9 Days post-anthesis (DPA) stained with calcofluor (channel 1) and whose demethylesterified pectins are labelled with the LM19 antibody (channel 2)</li> <li>2 genotypes: Col0 and iku2</li> <li>3 independent experiments</li> </ul> </li> <li>pELA1-VENUS-Col0-iku2-Fig3c <ul> <li>Confocal stack of developing seeds expressing <em>pELA1::3X-VENUS-N7</em></li> <li>2 genotypes: Col0 and iku2</li> <li>1 independent experiments</li> </ul> </li> <li>pELA1-VENUS-Col0-iku2-Supp Fig. 6 <ul> <li>Confocal stack of developing seeds expressing <em>pELA1::3X-VENUS-N7</em></li> <li>2 genotypes: Col0 and iku2</li> <li>1 independent experiments</li> </ul> </li> <li>Seed-size-ap26-iku2-Fig.5e-Supp-Fig. 12c <ul> <li>Pictures of dry seeds</li> <li>4 genotypes: Col0, iku2, ap2-6 and iku2 ap2-6</li> <li>2 independent experiments</li> </ul> </li> <li>Wall-rupture-Col0-iku2-Fig3k <ul> <li>Confocal stack of developing seeds expressing <em>LTi6b-GFP </em>(First channel) and dyed with FM4-64 (second channel) imaged after a 40 µm indentation to break testa wall</li> <li>2 genotypes: Col0 and iku2</li> <li>2 replicates</li> </ul> </li> <li>Wall-rupture-Col0-iku2-Supp Fig. 11 <ul> <li>Confocal stack of developing seeds expressing <em>LTi6b-GFP </em>(First channel) and dyed with FM4-64 (second channel) imaged after a 30 µm or a 50µm indentation to break testa wall</li> <li>2 genotypes: Col0 and iku2</li> <li>2 replicates</li> </ul> </li> <li>181211-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10 DPA</li> <li>2 genotypes: Col0 and iku2</li> </ul> </li> <li>190125-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10 DPA (no 9DPA)</li> <li>2 genotypes: Col0 and iku2</li> </ul> </li> <li>190523-Col0-ede13-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 9DPA</li> <li>2 genotypes: Col0 and <em>ede1-3</em></li> </ul> </li> <li>200724-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>iku2</em></li> </ul> </li> <li>200921-Col0-iku2-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>iku2</em></li> </ul> </li> <li>201023-Col0-ede13-Timelapse-Seed-Size <ul> <li>Pictures of developing seeds from 0 (ovules) days post-anthesis (DPA) to 10DPA</li> <li>2 genotypes: Col0 and <em>ede1-3</em></li> </ul> </li> <li>210519-Col0-Timelapse-invitro-Sorbitol <ul> <li>Picture of developing WT seeds (Col-0) at 3, 6, 9 and 12DPA</li> <li>Fruits were grown <em>in planta </em>(uncut) or <em>in vitro</em> from 3DPA onwards</li> <li>Fruits growing <em>in vitro</em> were cultivated in ½ MS + 1% Sucrose + 0.1X PPM (Plant Preservative Medium) + 1X Gamborg Vitamins + 0 to 200mM Sorbitol</li> <li>Note that the file containing the pictures from this experiment was too large for Zenodo, the pictures were thus transformed into 8 bit black/white pictures, a 2x2 binning was applied, before a saving into tiff. The original pictures are available at: <a href="http://flower.ens-lyon.fr/">http://flower.ens-lyon.fr/</a></li> </ul> </li> <li>210709-Col0-Timelapse-invitro-Sorbitol <ul> <li>Picture of developing WT seeds (Col-0) at 3, 6, 9 and 12DPA</li> <li>Fruits were grown <em>in planta </em>(uncut) or <em>in vitro</em> from 3DPA onwards</li> <li>Fruits growing <em>in vitro</em> were cultivated in ½ MS + 1% Sucrose + 0.1X PPM (Plant Preservative Medium) + 1X Gamborg Vitamins + 0 to 200mM Sorbitol</li> <li>Note that the file containing the pictures from this experiment was too large for Zenodo, the pictures were thus transformed into 8 bit black/white pictures, a 2x2 binning was applied, before a saving into tiff. The original pictures are available at: <a href="http://flower.ens-lyon.fr/">http://flower.ens-lyon.fr/</a></li> </ul> </li> <li>data.zip file: <ul> <li>Measurements of seed growth tracking experiments performed on WT plants as well as iku2 and ede1-3 mutants.</li> <li>Measurements of endosperm pressure performed on WT plants and iku2 mutants.</li> <li>Simulation results from a parameter space exploration of the system of ODEs we studied within the scope of the work described in the manuscript.</li> </ul> </li> </ul>
Supplementary datasets for manuscript bioRxiv https://doi.org/10.1101/2022.06.03.494642
<p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 1.</strong> </a> Database of reference human messenger RNAs and SARS-CoV-2 genomes used for mapping reads. Raw count matrices.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 2</strong></a><strong>.</strong> Expression matrices and transcript levels in uninfected cells.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 3.</strong> </a> Differential Expressed Gene analysis used in the volcano plots analysis. Datasets and results used in the Gene Ontology analyses. Classification of outliers.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 4.</strong> </a> Transcriptional profiles for all genes in each cell type.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 5.</strong> </a> Networks used in the analyses presented in Figs. 5 and 6.</p> <p> </p> <p><a href="https://drive.google.com/drive/folders/1xHsLC8ZDWJXm8f7fdXTcXr9hKszN3xpt?usp=sharing"><strong>Supplementary Data 6.</strong> </a> Transcriptional analysis of human astrovirus 1 infection in ileum organoids.</p>
Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins"
<p>Project files provided as supporting information to the manuscript "Fast, accurate, and system-specific variable-resolution modelling of proteins".</p> <p>The dataset contains the following files:</p> <p>- RMSD_adk: files containing the root-mean-square deviation computed on the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- RMSF_adk: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of adk (Figure 6).</p> <p>- APBS_adk: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 7).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the atomistic region of the ake protein, in the atomistic and CANVAS simulations (Figure 8).</p> <p>- RMSF_antibody: files containing the root-mean-square fluctuations of the C-alpha atoms/beads in the atomistic and CANVAS simulations of antibody (Figure 9).</p> <p>- APBS_antibody: PQR file of the system and DX file of the surface potential, for both the atomistic and CANVAS systems (Figure 10).</p> <p>- SASA_antibody: files containing the per-residue solvent accessible surface area of the hinge region of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure 11).</p> <p>- RMSIP_antibody: RMSIP between the essential subspaces computed from the atomistic and CANVAS simulations (Figure S3).</p> <p>- rgyr_antibody: files containing the radii of gyration of the antibody for each conformational cluster, in the atomistic and CANVAS simulations (Figure S4).</p> <p>- ake_AA.mp4: video of the all-atom simulation of adenylate kinase</p> <p>- ake_canvas.mp4: video of the canvas simulation of adenylate kinase</p>
Dataset used in GMD journal manuscript (egusphere-2022-1136)
<p>Dataset used in the manuscript submitted to Geoscientific Model Development.</p> <p><strong>Title:</strong> PyFLEXTRKR: a Flexible Feature Tracking Python Software for Convective Cloud Analysis</p> <p><strong>Authors: </strong></p> <p>Zhe Feng<sup>1</sup>, Joseph Hardin<sup>1</sup>, Hannah C. Barnes<sup>1,2,3</sup>, Jianfeng Li<sup>1</sup>, L. Ruby Leung<sup>1</sup>, Adam Varble<sup>1</sup>, Zhixiao Zhang<sup>4</sup></p> <p><sup>1</sup>Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington, USA</p> <p><sup>2</sup>Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder, Colorado, USA</p> <p><sup>3</sup>NOAA Global Systems Laboratory, Boulder, CO, USA</p> <p><sup>4</sup>Department of Atmospheric Sciences, University of Utah, Salt Lake City, Utah, USA</p>
Dataset for the paper "Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts"
<p>This dataset was used in the context of the article <em>Ground-truth Free Evaluation of HTR on Old French and Latin Medieval Literary Manuscripts</em>, at the Computational Humanities Research 2022 conference.</p> <p>Predictions.zip contains the XML ALTO for the HTR and Segmentation prediction of around 10 pages of 1900 manuscripts from the Bibliothèque nationale de France.</p> <p>Varying ground truth contains the original training material for training a classifier for CER classification (see the article).</p> <p>The ManuscriptsIIIF.csv contains metadata about the manuscripts.</p> <p>This work was funded by the DIM MAP under the CREMMALab funding.</p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Fake Manuscript 6 Skewer
/dream prompt:dark souls knight vs jedi sith lightsaber duel, hema medieval combat manuscript, woodcut print xylograph, by albrecht durer meyer wittenwiller, trending featured artstation width:704 height:384 number:4 steps:50 seed:4142567290 https://cdn.discordapp.com/attachments/1014674824313970699/1014683447857528933/dark_souls_knight_vs_jedi_sith_lightsaber_duel_hema_medieval_combat_manuscript_woodcut_print_xylograph_by_albrecht_du__width-704_height-384_number-4_steps-50_seed-3756230264_ts-1661989775_idx-0.png Source: Objaverse 1.0 / Sketchfab
Fake Manuscript 4
https://www.artbreeder.com/beta/image/0fdcf424c9cb55d908bfa333fd3a Source: Objaverse 1.0 / Sketchfab
Fake Manuscript 3
https://www.artbreeder.com/beta/image/98aab87f2e2a7d4034bdacac172c Source: Objaverse 1.0 / Sketchfab
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.