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3,197 results for “atlas”
CASSISjuice [atlas]
<p>Mid-infrared spectroscopy provides many important diagnostics on gas and dust features in a wide variety of astrophysical objects. In the JWST era, it is important to maintain a durable database of observations with previous facilities, for preparing new observations or completing existing ones. The Spitzer Infrared Spectrograph observed more than 20000 targets with wavelengths as low as 5.2um and as long as 38.0um, thereby complementing JWST/MIRI data for the long wavelength diagnostics and providing overall invaluable diagnostics together with JWST or in view of future IR facilities.</p> <p>In order to maximize the science output of Spitzer/IRS, the CASSIS atlas has provided reduced IRS spectra since 2011, selecting the best spectrum from various methods. We present CASSISjuice, an offline version of the pipeline (https://zenodo.org/record/8339954) and atlas (https://zenodo.org/record/8339965), adding several hundred sources that had never cleared the pipeline in order to make it complete for the first time.</p> <p>We updated the low- and high-resolution pipelines in order to be able to process every IRS staring mode observation (i.e., all observations but maps), and we also upgraded the high-resolution pipeline to version 2. The new pipeline also associates the pointings within ``cluster'' observations resulting in a single spectrum (possibly low- and high-resolution) per position and therefore overall a single CASSISjuice ID per targeted position. Independent observations at the same position are still not combined.</p> <p>The repositories, available at Zenodo and https://gitlab.com/cassisjuice, provide the open-source pipeline code and the atlas itself with specific attention to producing the smallest dataset possible. Python notebooks are included to illustrate the offline manipulation of the full atlas. The offline CASSISjuice atlas is meant to facilitate the analysis of large samples and the identification of potentially interesting or relevant spectra. </p>
Multilevel atlas comparisons reveal divergent evolution of the primate brain
<p>Nifti files of 20 mammalian atlases modified into a Common Multilevel Segmentation.</p> <p>(See Figure 1 in Multilevel atlas comparisons reveal divergent evolution of the primate brain; https://www.pnas.org/doi/full/10.1073/pnas.2202491119#sec-3)</p> <p>These nifti files are based on the brain atlases from 18 mammalian species, that were published between the years 2013 and 2021 (see list).</p> <p>The Python script to re-segment the "original" atlases into the modified version (that is shared here) is also available:</p> <p>see Modify_atlases.py</p> <p>Each species folder contains 5 nifti files: 1 for each level of segmentation and 1 for the brain segmentation.</p> <p>The other txt files are the volumetric output extracted using AFNI on each nifti file.</p> <p>Please read the Readme.txt file to credit and cite accordingly all the authors.</p> <p> </p> <p> </p> <p> </p> <p> </p>
MRI Brain Template and Atlas of the Mouse Lemur Primate Microcebus murinus
<p>MRI template and 120-region atlas for the mouse lemur primate Microcebus murinus.<br> <br> Generated from 34 animals aged 15-58 months old scanned at 7T using a T2-weighted sequence, resolution 115 × 115 × 230 µm. The code developed to create and manipulate the template has been refined into general procedures for registering small mammal brain MR images, available within a python module sammba-mri (SmAll-maMMals BrAin MRI; <a href="https://sammba-mri.github.io/">https://sammba-mri.github.io/</a>). The template was up-sampled to 91 µm isotropic for hand-segmentation of structures, and also used to create probability maps of grey matter, white matter and cerebro-spinal fluid.</p> <p>if used for publication please cite: </p> <p><strong>A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy</strong><br> Nachiket A Nadkarni, Salma Bougacha, Clément Garin, Marc Dhenain, Jean-Luc Picq<br> Jan 2019<br> <strong>NeuroImage</strong> 185, 85-95<br> DOI: 10.1016/J.NEUROIMAGE.2018.10.010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1053811918319694">https://www.sciencedirect.com/science/article/pii/S1053811918319694</a></p>
DTA Atlas: A Massive-Scale Drug Repurposing Database
<p>The database consists of affinity predictions on a wide selection of drugs versus all proteins in the human proteome from advanced deep neural networks.</p>
Supplementary data and scripts for "An Atlas of Human Metabolism"
<p>This repository contains the models, data, and scripts associated with the publication "An Atlas of Human Metabolism".</p> <p>The content is divided into three main directories, each containing their own README file containing instructions.</p> <ol> <li>tINIT_GEMs - Contains the genome-scale metabolic models (GEMs) generated in the study using the tINIT algorithm, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>ec_GEMs - Contains the enzyme-constrained models (ecGEMs) generated in the study using the GECKO framework, as well as many scripts and datasets necessary to reproduce model generation, analyses, and figures.</li> <li>GEM_PRO - Contains the GEM-PRO dataframe with the protein structure information associated with the Human1 model.</li> </ol>
Fig. 3 in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 3. Area codes as used in the atlas, from Fauna Europaea guidelines (Jong et al. 2014, supplementary material 1). Reproduced with permission. Note that MN (Montenegro) and SB (Serbia) in the above list and throughout this paper are shown as YU (Yugoslavia) on this map.
Fig. 1. A in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 1. A selection of European species of Julida. A. Boreoiulus tenuis (Bigler, 1913) (Blaniulidae) (J. Spelda phot.). B. Trichoblaniulus hirsutus (Brölemann, 1899) (Trichoblaniulidae) (D. Cheung phot.). C–J. Julidae. C. Cylindroiulus boleti (C.L. Koch, 1847) (J. Spelda phot.). D. Leptoiulus belgicus (Latzel, 1884) (J. Spelda phot.). E. Ommatoiulus hoffmani Akkari & Enghoff, 2012 (K. Mohr phot.). F. Ommatoiulus sabulosus (Linnaeus, 1758), colour variety from the Italian Riviera (D. Cheung phot.). G. Pteridoiulus aspidiorum Verhoeff, 1913 (J. Spelda phot.). H. Pachyiulus cattarensis (Latzel, 1884) (D. Antić phot.). I. Serboiulus deelemanni Strasser, 1971 (D. Antić phot.). J. Unciger foetidus (C.L. Koch, 1838) (J. Spelda phot.). Not to scale.
Neurobiology Research Unit – Serotonin Atlas
<p>A high-resolution positron emission tomography (PET)- and magnetic resonance imaging-based human brain atlas of four important serotonin receptors (5-HT1A, 5-HT1B, 5-HT2A, 5-HT4) and the serotonin transporter (5-HTT) is presented. The actual dataset includes five NIfTI images (one NIfTI for each serotonin tracer) and 10 FreeSurfer surfaces with average non-displaceable binding potential (BP<sub>ND</sub>) values (two Freesurfer surfaces for each serotonin tracer). The molecular imaging maps were related to autoradiography data and an unprecedented agreement was found, supporting the validity of the methodology and results presented, and allowing translating PET binding estimates into densities. This conversion facilitates the interpretability of the maps and allows for a direct comparison across the five 5-HT targets, in vivo in the human brain. All participants included in this study were healthy male and female controls from the Cimbi database; the data analysis was restricted to include individuals aged between 18 and 45 years. A total of 232 PET scans and corresponding structural MRI scans were acquired for 210 individual participants; 189 subjects had only one scan, 20 subjects had two scans, and a single had three scans. Scans were combined and aggregated atlases for each serotonin tracer were generated.</p> <p>For more information on the original data, please go to: <a href="https://xtra.nru.dk/FS5ht-atlas/">https://xtra.nru.dk/FS5ht-atlas/</a></p>
Redistribution of the shapefile with the watercourses of the Flemish Hydrographic Atlas (status 2020-08-07)
<p>This is a redistribution of a subdataset of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/020a452d-8cd2-41b7-9c64-2be367668837">Vlaamse Hydrografische Atlas - Waterlopen, 7 augustus 2020</a>’ (Flemish Hydrographic Atlas - Watercourses, 7th of August 2020), originally published by the Vlaamse Milieumaatschappij - afdeling Operationeel Waterbeheer (VMM, Flemisch Environmental Agency - division Operational Water Management) and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>The subdataset is a shapefile of lines that represent water courses in the Flemish region, identical to the shapefile <code>Vhag</code> in the original data source. The shapefile contains the centre axes of both navigable and non-navigable (classified) water courses, supplemented by some non-classified and non-navigable water courses. These water courses are part of the Flemish Hydrographic Atlas. The shapefile is maintained in a partnership of the Flemish provinces and Flemish regional institutions, with the division Operational Water Management of the VMM acting as the central manager.</p> <p>The data source is produced, owned and administered by the Flemish Environmental Agency - division Operational Water Management (VMM, Department of Environment of the Flemish government).</p>
CORDEX GCM source.grids for interpolating CORDEX data for ATLAS
<p>This is the list of all source.grid files used for the conservative interpolation of all the outputs from regional climate models from the CORDEX experiment used in ATLAS (https://www.ipcc.ch/report/ar5/wg1/atlas-of-global-and-regional-climate-projections/)</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
Datasets and supplemental information accompanying the corneal meta-atlas
<p>This repository currently contains datasets and files needed for cPredictor: <a href="https://github.com/Arts-of-coding/cPredictor">https://github.com/Arts-of-coding/cPredictor</a>.</p> <p> </p> <p>Additionally, "cornea_v1_pnas_nexus.h5ad" contains the integrated and pre-processed single-cell object with raw counts only.</p>
Circadian ontogenetic metabolomics atlas: an interactive resource with insights from rat plasma, tissues, and feces
<p>LC–MS instrumental files in mzXML format for metabolomics (HILICp, HILICn, HSST3p, HSST3n) and lipidomics platforms (LIPp, LIPn), including metadata for study samples, method blanks, quality control samples, and serial dilution samples. The instrumental files were acquired for each LC–MS platform as part of a study focused on creating a circadian ontogenetic metabolomics atlas of rat plasma, tissues, and feces. The original paper is accessible at http://doi.org/10.1007/s00018-025-05783-w</p>
Atlas of Heritage Trees
<p><strong>Atlas of Heritage Trees</strong><br>This dataset is a collection of laser scanned heritage trees of exceptional historical, cultural, and ecological significance. The trees have been archived in <em>laz</em>,<em> e57</em>, <em>pcd</em>, <em>ply</em>, <em>xyz</em>, and<em> 3dm</em> format point clouds. The project was directed by <a href="https://baharmon.github.io/">Brendan Harmon</a> and <a href="https://hynam.org/">Hye Yeon Nam</a>. Contributors include Cecil Chapman, Carlos Roman, Jocelynne Crandall, Javier Zamora, Huan Guo, William Reinhardt, Julie Whitbeck, Kaiti Fink, and Jeff Boucher. This project was funded by an LSU Big Idea Grant and the LSU Arts & Humanities Support Fund. It was supported by the LSU Center for Computation and Technology, the LSU Center for GeoInformatics, and the LSU Coastal Ecosystem Design Studio. The point clouds are released under the <a href="https://creativecommons.org/share-your-work/public-domain/cc0/">Creative Commons Zero</a> public domain dedication. See the collection online at <a href="https://xyz.cct.lsu.edu/">xyz.cct.lsu.edu</a>.</p> <p><strong>License</strong><br>This dataset is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">Creative Commons Zero 1.0</a> Universal Public Domain Dedication by Brendan Harmon.</p>
Population average atlas for BundleSeg
<p><strong>Multi-atlas bundle segmentation</strong></p> <p>This data is made to be used with the following script:<br><a href="https://github.com/scilus/scilpy/blob/2.1.1/scripts/scil_tractogram_segment_with_bundleseg.pyent_with_bundleseg.py">https://github.com/scilus/scilpy/blob/master/scripts/scil_tractogram_segment_with_bundleseg.py</a><br><br>Or the following Nextflow pipeline:<br><a href="https://github.com/scilus/rbx_flow">https://github.com/scilus/rbx_flow</a></p> <blockquote> <p>Etienne St-Onge, Kurt Schilling, Francois Rheault, "BundleSeg: A versatile, reliable and reproducible approach to whitte matter bundle segmentation.", arXiv, 2308.10958 (2023)<br><br>Rheault, François. "Analyse et reconstruction de faisceaux de la matière blanche." Computer Science (Université de Sherbrooke) (2020), https://savoirs.usherbrooke.ca/handle/11143/17255</p> </blockquote> <p><strong>Usage</strong><br>Here is an example (for more details use `scil_tractogram_segment_with_bundleseg.py -h`) :</p> <p><code>antsRegistrationSyNQuick.sh -d 3 -f ${T1} -m mni_masked.nii.gz -t a -n 4</code><br><code>scil_tractogram_segment_with_bundleseg.py ${TRACTOGRAM} config_fss_1.json atlas/*/ output0GenericAffine.mat --out_dir ${OUTPUT_DIR}/ --log_level DEBUG --minimal_vote 0.4 --processes 8 --seed 0 --inverse -f</code></p> <p>To facilitate interpretation, all endpoints were uniformized head/tail. To see, which side of a bundle is head or tail, you can load the atlas bundle into the software <a href="https://github.com/imeka/mi-brain">MI-Brain</a></p> <p><strong>Notes on bundles</strong><br>- AC and PC were added mostly in case the atlas is used for lesion-mapping or figures. Likely, segmentation won't produce good results. This is mostly due to difficult tracking for these bundles.<br>- The CC are split for each lobe. However, for technical consideration, the frontal portion was split in two to facilitate clustering and segmentation. For the same reason, the portion fanning to the pre/post central gyri were separated.<br>- The streamlines present in the CC are homotopic, Recobundles will allow for variation and thus lead to 'some' heterotopy. However, it is expected that the results will be mostly homotopic.<br>- CG has 3 possible endpoint locations. However, the full extent of the tail is difficult to track and is often missing.<br>- FPT and POPT should terminate in the pons. However, to fully capture candidate streamlines and improve segmentation quality even streamlines reaching down the brainstem are selected. <br>- PYT should reach down the brainstem. For similar reasons to the FPT/POPT, streamlines ending in the pons are selected. Otherwise, fanning is affected and bundles is too skinny. <br>- OR_ML will most likely have difficulty capturing the full ML. However, this is often due to difficult tracking.<br>- The cerebellum is often cut due to acquisition FOV. In such a case, all projection bundles will be more difficult to recognize and most cerebellum bundles will be missing (ICP, MCP, SCP).</p> <p>See Mosaic of bundles <a href="https://i.ibb.co/n7Ln3Gf/mosaic-local.png"><strong>here</strong></a>.</p> <p><strong>Acronym</strong><br>AC - Anterior commisure<br>AF - Arcuate fasciculus<br>CC_Fr_1 - Corpus callosum, Frontal lobe (most anterior part)<br>CC_Fr_2 - Corpus callosum, Frontal lobe (most posterior part)<br>CC_Oc - Corpus callosum, Occipital lobe<br>CC_Pa - Corpus callosum, Parietal lobe<br>CC_Pr_Po - Corpus callosum, Pre/Post central gyri<br>CC_Te - Corpus callosum, Temporal lobe<br>CG - Cingulum<br>FAT - Frontal aslant tract<br>FPT - Fronto-pontine tract<br>FX - Fornix<br>ICP - Inferior cerebellar peduncle<br>IFOF - Inferior fronto-occipital fasciculus<br>ILF - Inferior longitudinal fasciculus<br>MCP - Middle cerebellar peduncle<br>MdLF - Middle longitudinal fascicle<br>OR_ML - Optic radiation and Meyer's loop<br>PC - Posterior commisure<br>POPT - parieto-occipito pontine tract<br>PYT - Pyramidal tract<br>SCP - Superior cerebellar peduncle<br>SLF - Superior longitudinal fasciculus<br>UF - Uncinate fasciculus</p>
Atlas of Yucatec Maya Online
<p>presentation of the Atlas of Yucatec Maya online: project, aims, querying the online resource</p>
An exposome atlas of serum reveals risk of chronic diseases in Chinese population
<p>Although adverse environmental exposures are considered to be a major cause to chronic diseases, current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, we collected serum samples from 5696 healthy people and patients, including 12 chronic diseases in China, and completed serum biomonitoring containing 267 chemicals using gas and liquid chromatography-tandem mass spectrometry. 74 high-frequently detected exposures were used for exposure characterization and risk analysis. Results showed that region was the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances were associated with multiple chronic diseases, and some of them exceeded safe ranges. Mixture effect models showed significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provided a comprehensive human serum exposure atlas and its disease risk, which could guide subsequent more in-depth cause-and-effect studies between environmental exposures and human health. The R codes and related example data for statistical analysis and figure production have been deposited to the GitHub (https://github.com/youlei2023/ExposomeAtlas).</p>
Data behind The ALCHEMI atlas: principal component analysis reveals starburst evolution in NGC 253
<p>This depository is for additional files of the PCA paper using the ALCHEMI survey.</p> <p>std_datalist.csv: This is a csv file that includes standardized intensities for all the transitions/continua.</p> <p>pca_alchemi_corrmatrix.py: This is a python file to plot a correlation matrix of standardized intensities. It displays a transition pair when you hover the cursor on the matrix element. It uses std_datalist.csv.</p>
Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century
<h2>Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century (v1)</h2> <p><strong><a href="https://geo-ecclesiae.kul.pl/apps/latin-church-1772" rel="nofollow">The application "Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century"</a></strong> was prepared by the Institute for the Historical Geography of the Church in Poland on the basis of data published in the work of S. Litak with the same title (TNKUL, Lublin 2006). The original database prepared by B. Szady was created in 1993-1996 prior to the publication of the previous edition of Litak's study ("The Latin Church in the Polish-Lithuanian Commonwealth around 1772: Administrative Structures", Institute of East-Central Europe, Lublin 1996, Religious and Ethnic Communities in the Polish-Lithuanian Commonwealth in the Second Half of the 18th Century, 1). Work carried out over the next decade resulted in the development of the source base, additions and corrections. At the same time, a map was prepared and editorial changes were made regarding the system of source and bibliographic abbreviations.</p> <p>The information presented in the application and in the downloadable files has been grouped into 4 sections: 1) churches, 2) monasteries and convents, 3) ecclesiastical boundaries, 4) secular boundaries. The whole resource has been provided with appropriate metadata and list of abbreviations.</p>
BTyperDB: a community-curated, global atlas of Bacillus cereus sensu lato genomes for epidemiological surveillance
<p>The ability to cause foodborne illness, anthrax, and other infections has been attributed to numerous lineages within <em>Bacillus cereus sensu lato</em> (<em>s.l.</em>). However, existing pathogen surveillance databases facilitate dangerous pathogen misidentifications when applied to <em>B. cereus s.l.</em>, potentially hindering outbreak or bioterrorism attack response efforts. To address this, we developed BTyperDB (<a href="http://www.btyper.app/">www.btyper.app</a>), an atlas of <em>B. cereus s.l.</em> genomes with standardized, community-curated metadata. BTyperDB aggregates all publicly available <em>B. cereus s.l.</em> genomes (including >2,600 previously unassembled genomes) with novel genomes donated by laboratories around the world, nearly doubling the number of publicly available <em>B. cereus s.l.</em> genomes. To showcase its utility for pathogen surveillance, we use BTyperDB to identify emerging anthrax toxin- and capsule-harboring lineages. Overall, our study provides insight into the epidemiology of an under-studied group of emerging pathogens and highlights the benefits of inclusive, community-driven metadata FAIRification efforts.</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.