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Figure 2 in Mites occurring in farm buildings as allergic agents and indicators in forensic analyses
Figure 2 Dominance [%] of the Astigmatina mite species in all examined farm buildings.
Carbon and nitrogen abundances as indicators of material mixing in evolved stars
<p>Carbon and nitrogen abundances are among most useful quantitative indicators of mixing processes in evolved stars. Because of the first dredge-up abundances of 12C decrease while abundances of 13C and 14N increase. These alterations become efficient again on the red giant branch when stars reach the so-called luminosity bump, and depend on stellar evolutionary stage, mass, metallicity, rotation, magnetic activity and other parameters and processes. An overview is provided on observational analyses of evolved low mass giants accomplished using the Nordic Optical Telescope.</p>
Simultaneous determination method of 10 organophosphate esters in oyster, an environmental indicator organism
<p><span>Oyster is a common indicator organism of environment pollution. A method was built to simultaneously determine ten common commercial organophosphate ester flame retardants in oysters. After the careful optimization of conditions, the organophosphate esters (OPEs) in oyster sample were extracted with hexane assisted by ultrasound, purified with NH2 solid-phase extraction column, eluted with the mixture solution of dichloromethane and ethyl acetate at the ratio of 1:1 (v/v), and finally detected by gas chromatography coupled with tandem triple quadrupole mass spectrometry (GC–MS/MS). All the ten compounds in the concentrations ranging from 0.5 ng·mL-1 to 100 ng·mL-1 showed good linearity with their R2 higher than 0.9991. The limits of detection and limits of quantification for the organophosphate esters were from 0.03 ng·g-1 to 0.47 ng·g-1 (dry weight) and from 0.1 ng·g-1 to 1.56 ng·g-1 (dry weight), respectively. At the three spiking levels with five replicates, recoveries of organophosphate esters ranged from 82% to 108% with the relative standard deviation ranging from 0.9% to 6.4%. In the samples collected from mariculture farms, nine OPE compounds were detected in oysters with the concentration ranging from 6.52 ng·g-1 to 22 ng·g-1 (dry weight). It indicated the method was suitable for OPEs determination in oysters.</span></p>
Quasi-Armored Mud Clasts as Indicators of Transport Processes of Subaqueous Sediment Gravity-Flow
<p>Fig. S1 Quasi-armored mud clasts in different case studying. (A) Quasi-armored mud clasts in the H1 division of Hybrid event bed, Eocene Liushagang Formation; (B) Quasi-armored mud clasts in massive sandstone, Early Cretaceous Lingshandao Formation; (C) Quasi-armored mud clasts in massive sandstone, Early Cretaceous, North Falkland Basin (Dodd et al., 2018); (D) Quasi-armored mud clasts in the upper part of massive sandstone, Middle Eocene, Ainsa System (Pickering et al., 2015); (E) and (F) Quasi-armored mud clasts in massive sandstone, Upper Jurassic, North Sea rift system (Jackson et al., 2011); (G) Quasi-armored mud clasts in the H3 division, Late Jurassic, in the northern North Sea (Haughton et al., 2003) ; (H) and (I) Quasi-armored mud clasts in massive sandstone, Late Jurassic, in the northern North Sea (Williams, 2015).</p> <p>Fig. S2 (A) Location of Beibuwan Basin in China. (B) Location of Weixi’nan depression in the Beibuwan Basin. (C) Sedimentary facies distribution of the upper member of the Liushagang Formation in the Weixi’nan depression. (D) Sedimentary sequence and stratigraphic framework of the Weixinan Depression, Beibuwan Basin (Huang et al., 2013).</p> <p>Fig. S3 The lateral distribution of gravity-flow deposits in the Upper of the Liushagang Formation in the research area from proximal to distal. For the location of the section, see Fig. S2 C.</p> <p>Fig. S4 Facies analysis of Well WZ11-7-1 (in relatively distal), dominated by hybrid event beds with common quasi-armored mud clasts in different units.</p> <p>Fig. S5 Facies analysis of Well WZ11-7-4 (in relatively proximal), dominated by high-density turbidities beds with occasional quasi-armored mud clasts. For the legend of facies analysis, see Fig. S4.</p>
Data from: Different taxonomic and functional indices complement the understanding of herb-layer community assembly patterns in a southern-limit temperate forest
<p><span>The efficient conservation of vulnerable ecosystems in the face of global change requires a complete understanding of how plant communities respond to various environmental factors. We aim to demonstrate that a combined use of different approaches, traits, and indices representing each of the taxonomic and functional characteristics of plant communities will give complementary information on the factors driving vegetation assembly patterns. We analyzed variation across an environmental gradient in taxonomic and functional composition, richness, and diversity of the herb-layer of a temperate beech-oak forest that was located in northern Spain. We measured species cover and four functional traits: leaf dry matter content (LDMC), specific leaf area (SLA), leaf size, and plant height. We found that light is the most limiting resource influencing herb-layer vegetation. Taxonomic changes in richness are followed by equivalent functional changes in the diversity of leaf size but by opposite responses in the richness of SLA. Each functional index is related to different environmental factors even within a single trait (particularly for LDMC and leaf size). To conclude, each characteristic of a plant community is influenced by different and even contrasting factors or processes. Combining different approaches, traits, and indices simultaneously will help us understand how plant communities work.</span></p>
COVID-19: data and indicators to measure the return to work in Area Science Park after the emergency phase
<p>Data to measure the impact of SARS-CoV-2 virus on work, activities and services of Area Science Park and the return to work after the emergency epidemiological phase.</p> <p>Files available:</p> <ul> <li>Report Restart Area</li> <li>Stima_Presenze_Area_Science_Park</li> </ul> <p> </p> <p> </p>
Indices and sign arrays for floretion multiplication up to order 8.
<div> <div> <div> <p>A floretion is a type of hypercomplex number that extends the concept of quaternions. One main characteristic of floretions is their representation in an octal number system, where the digits are limited to {1, 2, 4, 7} which, when endowed with multiplication, become a discrete mathematical group. The number of digits in octal is the "order" of the group. These numbers can be associated with geometric shapes, specifically equilateral triangles, which are used to represent complex operations and properties visually.</p> <p>In mathematical operations, floretions employ logic operations like XOR for multiplication, following rules akin to quaternion multiplication but extended into higher dimensions and complexity. Floretions can be manipulated algebraically or graphically, each method offering unique insights into their structure and behavior.</p> <p>The development and use of floretions incorporate both algebraic structures and geometric representations, bridging a unique connection between number theory and visual geometry.</p> <p><a title="Floretion Calculator " href="https://floretions.com/">Floretion Calculator: floretions.com</a></p> <p><a href="https://github.com/Floretion-Inquisitor/floretions">Floretions at GitHub</a></p> <p> </p> <p> </p> </div> </div> </div> <p><strong>Dataset Description for Floretion Multiplication (up to order 8):</strong></p> <p>This collection of datasets, stored in numpy binary format, is designed to facilitate efficient multiplication of floretions up to order 8. The matrices included—indices and signs—are optimized for rapid computation of the product <strong>z=x⋅y</strong> using standard dot products, enhancing both speed and efficiency.</p> <p>For a foundational understanding, consider quaternion multiplication (order 1 floretions):</p> <h3>x = x[0]i + x[1]j + x[2]k + x[3]e </h3> <h3>y = y[0]i + y[1]j + y[2]k + y[3]e<code><br></code></h3> <p><strong>Multiplication Outcome</strong>: The coefficient for base vector "<strong>i</strong>" in <strong>z=x⋅y</strong> is calculated as:</p> <div><code>x[0]y[3]+x[1]y[2]−x[2]y[1]+x[3]y[0]=np.dot(np.multiply([1,−1,1,1],[x[3],x[2],x[1],x[0]]),[y[0],y[1],y[2],y[3]])</code></div> <p>Notice that <code>[1,−1,1,1]</code> above is the first row in the "signs" matrix and the indices of the coefficients of <strong>x</strong> are given by the first row of the "indices" matrix, below. </p> <h3>"Signs" Matrix<br>[[ 1 -1 1 1]<br>[ 1 1 -1 1]<br>[-1 1 1 1]<br>[-1 -1 -1 1]]</h3> <h3>"Indices" Matrix<br>[[3 2 1 0]<br>[2 3 0 1]<br>[1 0 3 2]<br>[0 1 2 3]]</h3> <p>These precomputed matrices streamline the computation of floretion products across various orders. For order 7 floretions, the data is split into 4 segments, for order 8 into 64 segments to keep file sizes managable. </p> <p> </p> <ul> <li> <div> <ul> <li> </li> </ul> </div> </li> </ul> <p> </p> <p> </p> <p> </p>
Supplementary figures and data for "Complete phylogeny of Micrathena spiders suggests multiple dispersal events among Neotropical rainforests, islands, and landmasses, and indicates Andean orogeny promotes speciation"
<p>Supplementary figures and data for "Complete phylogeny of <em>Micrathena</em> spiders suggests multiple dispersal events among Neotropical rainforests, islands, and landmasses, and indicates Andean orogeny promotes speciation" by Ivan L. F. Magalhaes, Pedro H. Martins, Bárbara T. Faleiro, Teofânia H. D. A. Vidigal, Fabrício R. Santos, Leonardo S. Carvalho, Adalberto J. Santos, submitted to <em>Cladistics</em>. </p> <p>Supplementary Figures S1 – S15</p> <p>Supplementary data S16: Complete specimen data of specimens used as vouchers for morphological scorings in Excel spreadsheet format.<br>Supplementary data S17: Discrete character matrix with morphological scorings in Nexus format.<br>Supplementary data S18: Complete specimen data of specimens used as vouchers for DNA sequencing in Excel spreadsheet format.<br>Supplementary data S19: Alignment of cytochrome oxidase I sequences in Fasta format.<br>Supplementary data S20: Alignment of histone H3 sequences in Fasta format.<br>Supplementary data S21: Alignment of 28S ribosomal sequences in Fasta format.<br>Supplementary data S22: Alignment of internal transcribed spacer sequences in Fasta format.<br>Supplementary data S23: Alignment of 16S ribosomal sequences in Fasta format.<br>Supplementary data S24: Zipped folder containing the inputs for estimating the phylogeny using morphological data in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S25: Zipped folder containing the inputs for estimating the phylogeny using sequence data aligned with Muscle in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S26: Zipped folder containing the inputs for estimating the phylogeny using sequence data aligned with MAFFT in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S27: Zipped folder containing the inputs for estimating the phylogeny using morphological and sequence data aligned with MUSCLE in IQ-Tree: matrix in nexus format, partition file, and command file in plain text.<br>Supplementary data S28: Input Nexus for estimating the phylogeny using morphological data in MrBayes.<br>Supplementary data S29: Input Nexus for estimating the phylogeny using sequence data aligned with Muscle in MrBayes.<br>Supplementary data S30: Input Nexus for estimating the phylogeny using sequence data aligned with MAFFT in MrBayes.<br>Supplementary data S31: Input Nexus for estimating the phylogeny using morphological and sequence data aligned with Muscle in MrBayes.<br>Supplementary data S32: Input XML for estimating the dated phylogeny under Bayesian inference in BEAST.<br>Supplementary data S33: Distribution maps for all Micrathena species included in this study, the database with the original records and their source, and the R script used to generate the maps.<br>Supplementary data S34: Zipped folder containing all the inputs for estimating ancestral ranges and performing biogeographic stochastic mapping in R. This contains (0) the scripts for performing model selection and estimating ancestral states over a distribution of posterior trees, followed by stochastic mapping; (1) the geography and tree files (MMCT, and 100 trees randomly drawn from the posterior distribution); (2) the dispersal matrix and (3) the files for performing the time-stratified analysis to test the influence of the closure of the Isthmus of Panama on Micrathena dispersal.<br>Supplementary data S35: R script for generating the in-situ-speciation-through-time graphs from the results of biogeographic stochastic maps, as seen in Fig. 5.<br>Supplementary data S36: R script for getting and plotting elevations from individual records of Micrathena.<br>Supplementary data S37: R script for performing the GeoHiSSE estimation of net diversification rates among areas.<br>Supplementary data S38: Zipped folder containing the optimal trees found as a result of analysing inputs S24–S32 (IQ-Tree, MrBayes and BEAST).<br>Supplementary data S39: Zipped folder containing the ancestral range estimation results under competing models in BioGeoBEARS.<br>Supplementary data S40: Excel spreadsheet with parameter values of competing biogeographic models in BioGeoBEARS.<br>Supplementary data S41: Excel spreadsheet with results of biogeographic stochastic mapping over 100 different trees from the posterior distribution; includes all Micrathena species.<br>Supplementary data S42: Excel spreadsheet with results of biogeographic stochastic mapping over 100 different trees from the posterior distribution; includes only Micrathena species which had their DNA sequenced.<br>Supplementary data S43: Excel spreadsheet with the geography matrix, indicating which Micrathena species may be found in each area, and indicating the proportion of species from each area that are missing from the incomplete dataset (i.e., excluding species with unavailable DNA sequences).<br>Supplementary data S44: Excel spreadsheet with parameter values of competing biogeographic models estimated in GeoHiSSE.</p>
Data affiliated with "Solid bitumen as an indicator of petroleum migration, thermal maturity, and contact metamorphism: A case study in the Barrandian Basin (Silurian - Devonian), Czech Republic"
<p>Fourier transform infrared (FTIR) spectra affiliated with the publication "<span>Solid bitumen as an indicator of petroleum migration, thermal maturity, and contact metamorphism: A case study in the Barrandian Basin (Silurian - Devonian), Czech Republic" in International Journal of Coal Geology (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.coal.2024.104493" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.coal.2024.104493</span></a>).<br></span></p> <p><span>The FTIR data are saved as CSVs under the numbers and names corresponding to Table in Supplement 1. Columns A corresponds to wavenumbers in cm-1, columns B and C to absorbance measured in two measurements.</span></p>
Code&Data_'Choosing fit-for-purpose biodiversity impact indicators for agriculture in the Brazilian Cerrado ecoregion'
<p>File "R_Scripts_biodiversity_indicators.zip" includes codes for calculating the biodiversity impact on the terrestrial vertebrates of the Cerrado biome using the countryside Species Area Relationship (cSAR), the Species Threat Abatement and Restoration (STAR) metric and the Species Habitat Index (SHI).</p> <p>File "results_biodiversity_indicators.zip" includes the result tables of the calculations done with the above mentioned codes. </p>
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
Machine learning based on whole-rock geochemical data: An indication for the porphyry Cu-Au deposits in Central Asian Orogenic Belt
<div> <h2>Introduction</h2> </div> <p>Welcome to the PorphyryAuML project! This repository is dedicated to exploring the mechanism of gold (Au) enrichment in porphyry systems within the Central Asian Orogenic Belt using machine learning. We apply models like XGBoost and Random Forest to analyze whole-rock geochemical data, aiming to classify porphyry deposit types and highlight key geochemical indicators.</p> <div> <h2>Key Features 🌟</h2> </div> <ul> <li><strong>Principal Component Analysis (PCA):</strong> Reduce dimensionality to discover the most significant variables.</li> <li><strong>Machine Learning Models:</strong> Utilize XGBoost and Random Forest for robust classification.</li> <li><strong>Feature Importance Analysis:</strong> Identify crucial geochemical markers for Au presence and quantity.</li> <li><strong>Visualization:</strong> Detailed plots to illustrate model outcomes and geochemical patterns.</li> </ul> <div> <h2>Data 📊</h2> </div> <div> <h3>DATA.xlsx</h3> </div> <p>The <code>DATA.xlsx</code> file contains crucial geochemical data used in our analysis, organized across three sheets:</p> <ul> <li><strong>group1</strong>: Represents the Cu-Au (Copper-Gold) porphyry deposits. This sheet contains all relevant geochemical markers and measurements specific to this group.</li> <li><strong>group2</strong>: Corresponds to Cu(-Au±Mo) (Copper with minor Gold and possibly Molybdenum) porphyry deposits. It includes a detailed set of data focusing on the variations and characteristics of these mixed element deposits.</li> <li><strong>group3</strong>: Contains data related to Cu-Mo (Copper-Molybdenum) porphyry deposits, focusing on the distinct geochemical profiles that typify these deposits.</li> </ul> <p>Each sheet is named to reflect the group it represents and is vital for our machine learning analysis to classify and predict porphyry deposit types based on their geochemical properties.</p>
Comprehensive clinical and genetic analyses of circulating bile acids and their associations with diabetes and its indices
<p>Files containing source data for "Comprehensive clinical and genetic analyses of circulating bile acids and their associations with diabetes and its indices," by Choucair et al., published in <em>Diabetes</em>.</p>
Secondary_data_Maximum chemotactic indices (Icmax) to DMSP, DMS and acrylate
<p>Dataset containing secondary data regarding Chemotactic Indexes (Ic). This data was used for the generation the plot about Ic related to the article: <strong>The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds.</strong> Therefore, provides information about the chemotaxis indexes obtained three cultured dinoflagellates (<em>Karlodinium armiger, Gyrodinium dominans</em> and<em> Oxyrrhis marina</em>) responding to gradients of DMSP, DMS and and acrylate diffusing from a microcapillary. </p> <p>Nomenclature:</p> <p>Cap: Capillary (C=control, E=experimental (with the chemical cue).</p> <p>D=DMSP/ M=DMS/ A=ACRYLATE</p> <p>2=2uM / 20= 20uM / 200= 200uM</p> <p>_1, _2, _3 = replicates 1, 2 or 3 </p>
Open Data in German Forest Information Systems: Towards an EU Forest Resilience Monitor (Collection of Forest Resilience Indicators)
<p>This dataset represents the collection of forest indicators for my Master's Thesis in the pioneer master programme at the Universities of Münster, Tallinn (Taltech) and Leuven (KUL), titled "Open Data in German Forest Governance: Towards an EU Forest Resilience Monitor". </p> <p>It contains a classification of inicators sourced from:</p> <ul> <li> <p><span>Nikinmaa, L., Lindner, M., Cantarello, E., Jump, A. S., Seidl, R., Winkel, G., & Muys, B. (2020). Reviewing the use of resilience concepts in forest sciences. <em>Current Forestry Reports, 6</em>, 61-80.</span></p> </li> <li> <p><span>European Commission. (2023a). Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on a monitoring framework for resilient European forests. <em>Directorate-General for Environment</em>. Lastly retrieved on December 17, 2023 from </span><span><a href="https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en"><span>https://ec.europa.eu/transparency/documents-register/detail?ref=COM(2023)728&amp;lang=en&lang=en</span></a></span><span>. </span></p> </li> <li> <p><span>European Parliament. (2024, February 27). European Parliament legislative resolution of 27 February 2024 on the proposal for a regulation of the European Parliament and of the Council on nature restoration (COM(2022)0304 – C9-0208/2022 – 2022/0195(COD)). Lastly retrieved on April 26, 2024 from </span><span><a href="https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html"><span>https://www.europarl.europa.eu/doceo/document/TA-9-2024-0089_EN.html</span></a></span><span>. </span></p> </li> </ul>
Quantifying Progress: Metrics and Indicators for Measuring Digital Transformation Maturity in Organizations
<p><span>As organizations increasingly embark on digital transformation journeys, the need for effective metrics and indicators to measure progress and maturity becomes paramount. This paper investigates the development and application of metrics for quantifying digital transformation maturity in organizations. Through an extensive review of literature and examination of case studies, the paper identifies key dimensions and stages of digital maturity. It proposes a framework encompassing both quantitative and qualitative metrics that can be used to assess an organization's digital transformation journey. The paper explores challenges associated with defining meaningful metrics and offers insights into adapting measurement frameworks to diverse organizational contexts. By addressing this critical gap in the literature, the paper aims to provide practitioners, researchers, and decision-makers with a valuable resource for evaluating and benchmarking digital transformation progress, fostering a more nuanced understanding of the multifaceted nature of organizational digital maturity.</span></p>
Fig. 4 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 4 Parity rate of Anopheles gambiae (s.l.) collected in treated and control areas
Fig. 1 in Benthic hydrozoan assemblages as potential indicators of environmental health in a mediterranean marine protected area Abstract
Fig. 1: Sampling stations and associated level of impact in the study area.
Lausanne environmental data : Sentinel L1C Lausanne region data - NDVI, ARVI indices; Walkable network
<p>Multi-spectral band images captured by the Sentinel L1C satellites in August 2016 over Lausanne, Switzerland. Bands B02, B03, B04, B08 projected according to the Swiss coordinates system (EPSG:21781- CH1903 / LV03 -- Swiss CH1903 / LV03) and associated calculated ARVI and NDVI vegetation indices.</p> <p>Walkable network (2021) from OpenStreeMap using OSMnx python package</p>
Data from: Fossils indicate marine dispersal in osteoglossid fishes, a classic example of continental vicariance
<p>The separation of closely-related terrestrial or freshwater species by vast marine barriers represents a biogeographic riddle. Such cases can provide evidence for vicariance, a process whereby ancient geological events like continental rifting divided ancestral geographic ranges. With an evolutionary history extending tens of millions of years, freshwater ecology, and distribution encompassing widely separated southern landmasses, osteoglossid bonytongue fishes are a textbook case of vicariance attributed to Mesozoic fragmentation of the Gondwanan supercontinent. Largely overlooked fossils complicate the clean narrative invoked for extant species by recording occurrences on additional continents and in marine settings. Here we present a new total-evidence hypothesis for bonytongue fishes combined with quantitative models of range evolution and show that the last common ancestor of extant osteoglossids was likely marine, and that the group colonized freshwater settings at least four times when both extant and extinct lineages are considered. The correspondence between extant osteoglossid relationships and patterns of continental fragmentation therefore represents a striking example of biogeographic pseudocongruence. Contrary to arguments against vicariance hypotheses that rely only on temporal or phylogenetic evidence, these results provide direct palaeontological support for enhanced dispersal ability early in the history of a group with widely separated distributions in the modern day.</p>
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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.