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30,813 results for “type”
GERONTE H2020 project - GERDAT006 - Dataset of symptoms and proms for specific cancer types and gender
<p>This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment</p>
DIRECTLib - a library of global optimization problems for DIRECT-type methods
<p><strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong></p> <p>In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein).</p> <p>File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: <a href="https://arxiv.org/abs/2109.14912">https://arxiv.org/abs/2109.14912</a></p> <p><strong>References</strong></p> <ol> <li>Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>.</li> <li> <p>R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>.</p> </li> <li> <p>L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, <a href="https://doi.org/10.1016/j.amc.2020.125596">DOI: 10.1016/j.amc.2020.125596</a></p> </li> <li> <p>L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, <a href="https://doi.org/10.1007/s11590-021-01726-z">DOI: 10.1007/s11590-021-01726-z</a></p> </li> </ol>
Analysis of the interacting residues between wild type SARS-CoV-2 spike protein and natural ligand hACE2, as well as three engineered alternative ligands
<p>The analysis of residue interactions between the SARS-CoV-2 spike protein and its natural (hACE2 <sup>1</sup>) and engineered binders P17 Fab <sup>2</sup>, Ty1 VHH <sup>3</sup> and LCB1 peptide <sup>4</sup> reveals that glutamine, serine and especially tyrosine residues on the ligand side are more frequent and influence spike binding efficiency, and that spike residues Glu484, Phe486, Tyr489 and Gln493 are more recurrent targets for interactions with ligands. The list of residues establishing contacts between the wild type structure of the SARS-CoV-2 spike protein and the binders defined above are described in Table 1. In Figure 1, the frequency and type of amino acids that interact with each spike residue is illustrated.</p>
Constructions of type "leat jođus", "leat johtimin" in North Saami and Inari Saami subcorpuses of SIKOR
<p>This data set contains the occurrences of constructions of types <em>leat jođus</em>, <em>leat johtimin</em> in North Saami and <em>leđe joođoost</em>, <em>leđe jotemin</em> in Inari Saami, extracted from the SIKOR korpus. The data is processed so that infinitive, illative and comitative complements of the construction have been marked out (based on original automated analysis of the corpus and corrected manually).</p> <p>The data is further analyzed in a paper published in <em>Journal de la Société Finno-Ougrienne</em> 98: <a href="https://doi.org/10.33340/susa.97223">https://doi.org/10.33340/susa.97223</a>.</p>
Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization
<p>Data and R code accompanying the publication "Range shifts of overwintering birds depend on habitat type, snow conditions and habitat specialization"</p> <p>Bosco L, Xu Y, Deshpande P, Lehikoinen A</p> <p>2022</p> <p>---------</p> <p>The data and code to calculate range shifts based on the center of gravity are provided here.</p> <p>The RData files contains raw data from the winter bird counts with added average snow depth values downloaded from open source databases (described in the paper), 100x100km grid info (grid ID, centroid coordinates and average (geographical) coordinates).</p> <p>The csv file contains the route lengths from the winter bird count transects per habitat type.</p> <p>The R file contains the R code used to clean the data (see methods in the publication) and calculate the habitat specific center of gravity (based on bird densities) which were used to calculate shift direction and distance.</p>
Data files for figures in "Characteristics of the two types of Kuroshio large meanders in the Shikoku Basin"
<p>Processed data files used to create the figures in the paper "Characteristics of the two types of Kuroshio large meanders in the Shikoku Basin".</p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
2022 Rice Crop-type Data for Western Tanzania
<p>Rice Crop-type data from Katavi Region Tanzania was collected by the NASA Harvest Program at the University of Maryland, the Sokoine University of Agriculture, and Flamingoo Food Limited under the Optimizing Crop Yield Data Collection for Supply Chain Enhancement project (more at: https://cropanalytics.net/optimizing-yield-data/) funded by ENABLING CROP ANALYTICS AT SCALE (ECAAS) is a multi-phase initiative that aims to catalyze the development, availability, and uptake of agricultural ground and remote sensing data and applications in smallholder production systems more at (https://cropanalytics.net/)</p>
Investigating Types and Survivability of Performance Bugs in Mobile Apps
<p>Replication package of the paper entitled "Investigating Types and Survivability of Performance Bugs in Mobile Apps" published in The Empirical Software Engineering Journal</p>
Supplementary material for "Patterns of high-flying insect abundance are shaped by landscape type and abiotic conditions"
<p><strong>Abstract</strong></p> <p>Insects are of increasing conservation concern as a severe decline of both biomass and biodiversity have been reported. At the same time, data on where and when they occur in the airspace is still sparse, and we currently do not know whether their density is linked to the type of landscape above which they occur. Here, we combine data of high-flying insect abundance from six locations across Switzerland representing rural, urban and mountainous landscapes, which was recorded using vertical-looking radar devices. We analysed the abundance of high-flying insects in relation to meteorological factors, daytime, and type of landscape. Air pressure was positively related to insect abundance, wind speed showed an optimum, and temperature and wind direction did not show a clear relationship. Mountainous landscapes showed a higher insect abundance than the other two landscape types. Insect abundance increased in the morning, decreased in the afternoon, had a peak after sunset, and then declined again, though the extent of this general pattern slightly differed between landscape types. We conclude that the abundance of high-flying insects is not only related to abiotic parameters, but also to the type of landscapes. Thus, conservation measures implemented on the ground should start to also account for the needs of high-flying insects.</p>
Raw data for the article entitled "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"
<p>Raw data for the plots in the open access article "Facile Solution Synthesis, Processing and Characterization of n- and p-Type Binary and Ternary Bi–Sb Tellurides"</p>
The winds of young Solar-type stars in the Hyades - Quiet Sun model
<p>This is the quiet Sun model from my MNRAS paper "The winds of young Solar-type stars in the Hyades"(https://doi.org/10.1093/mnras/stab1696). Please see the paper for a full description.</p>
Catalog of PAM and MBON cell types
<p>A catalog of some of the published anatomical findings on DAN PAM and MBON cell types in the mushroom body of <em>Drosophila melanogaster.</em> Major source is the major table in Aso <em>et al. </em>2014 (https://doi.org/10.7554/eLife.04577). Also includes results from other papers and combines into a single spreadsheet.</p>
Characterization of the polyspecific transferase of murine type I fatty acid synthase (FAS) and implications for polyketide synthase (PKS) engineering
<p><strong>Characterization of the polyspecific transferase of murine type I fatty acid synthase (FAS) and implications for polyketide synthase (PKS) engineering</strong></p> <p><a href="https://dx.doi.org/10.1021/acschembio.7b00718">https://dx.doi.org/10.1021/acschembio.7b00718</a></p> <p><strong>Abstract</strong></p> <p>Fatty acid synthases (FASs) and polyketide synthases (PKSs) condense acyl compounds to fatty acids and polyketides, respectively. Both, FASs and PKSs, harbor acyltransferases (ATs), which select substrates for condensation by β-ketoacyl synthases (KSs). Here, we present the structural and functional characterization of the polyspecific malonyl/acetyltransferase (MAT) of murine FAS. We assign kinetic constants for the transacylation of the native substrates, acetyl- and malonyl-CoA, and demonstrate the promiscuity of FAS to accept structurally and chemically diverse CoA-esters. X-ray structural data of the KS-MAT didomain in a malonyl-loaded state suggests a MAT-specific role of an active site arginine in transacylation. Owing to its enzymatic properties and its accessibility as a separate domain, MAT of murine FAS may serve as versatile tool for engineering PKSs to provide custom-tailored access to new polyketides that can be applied in antibiotic and antineoplastic therapy.</p> <p><strong>Raw dataset for protein databank accession code (PDB) 5my0</strong></p> <p><a href="http://dx.doi.org/10.2210/pdb5my0/pdb">http://dx.doi.org/10.2210/pdb5my0/pdb</a></p>
Measure While Drilling (MWD) dataset with rock type labels for 15 Norwegian hard rock tunnels
<p>The dataset is presented in the paper: </p> <p><em>Building and analysing a labelled Measure While Drilling dataset from 15 hard rock tunnels in Norway</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper has a preprint on SSRN: <a href="http://dx.doi.org/10.2139/ssrn.4729646" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.4729646</a> and is under review in a peer-reviewed journal.</p> <p>The dataset is utilised in a machine learning analysis in the paper:</p> <p><em>Predicting rock type from MWD tunnel data using a reproducible ML-modelling process</em>, by T.F. Hansen, Z. Liu, J. Torressen</p> <p>The paper is published in the journal <em>Tunnelling and Underground Space Technology</em>: </p> <p><a href="https://doi.org/10.1016/j.tust.2024.105843">https://doi.org/10.1016/j.tust.2024.105843</a></p> <p> </p> <p><strong>Description of the dataset:</strong></p> <p>Measure While Drilling (MWD) is a technique in rock drilling, mainly used in drill and blast tunnelling, where data about the rock mass is registered by sensors while drilling. The extensive and geologically diversified dataset contains corresponding MWD-data and rock mass mappings for 5205 blasting rounds from 15 hard rock tunnels in Norway. MWD-data are presented as tabular data. 10 different rocktypes are the corresponding labels.</p> <p>Four files are given:</p> <ul> <li>A csv-file of the training dataset - with outliers removed</li> <li>A csv-file of the testing dataset (split train/test 0.75/0.25) - with outliers removed</li> <li>A csv-file with the full unsplitted dataset, cleaned and with outliers removed</li> <li>A csv-file with the raw dataset, before cleaning, processing and outlier removal</li> </ul> <p>The author gratefully acknowledge the tunnel software/hardware company Bever Control, which have facilitated data from the clients Bane NOR, Statens Vegvesen, Nye Veier, and the contractor AF-Gruppen.</p> <p> </p> <p><strong>NOTE:</strong> The dataset is only available for research, no commercial use.</p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Supplementary Material to "Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)"
<p><span>Here we briefly describe the supplementary materials for the publication “Partial melting of amphibole–clinozoisite eclogite at the pressure maximum (eclogite type locality, Eastern Alps, Austria)” in the European Journal of Mineralogy, 35(5), 715-735 Schorn, S., Rogowitz, A., & Hauzenberger, C. A. (2023).</span></p>
Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"
<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p> 1. <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br> 2. <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p> • Remote Sensing Reflectance (Rrs)<br> • Pure Water Absorption (aw)<br> • Absorption Coefficient of Detritus (ad)<br> • Total Absorption Coefficient without Pure Water (agp)<br> • Absorption Coefficient of Phytoplankton (aph)<br> • Backscattering Coefficient of Total Particulate Matter (bbp)<br> • Scattering Coefficient of Total Particulate Matter (bp)<br> • Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p> • Chlorophyll a Concentration (Chl)<br> • Inorganic Suspended Matter Concentration (ISM)<br> • Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br> • Single-Scattering Albedo of Detritus at 550 nm (A_d)<br> • Power Law Exponent of Detritus Attenuation (G_d)<br> • Water Salinity (Sal)<br> • Water Temperature (Temp)<br> • Fraction for Diminished Coccolithophore Absorption (a_frac)<br> • Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p> • Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a><br> • OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p> 1. OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606<br> 2. Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br> 3. Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br> 4. Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p> • Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers<br> • Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>
Replication Package for the Paper Titled "How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?"
<p>This is a replication package for the article 'How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?'. The survey questions can be found here, and we encourage the survey to be re-used.</p> <p>We also include survey data (with demographic data and qualitative responses removed for anonymity reasons).</p> <p>The project team consists of Tracy Hall, Emily Winter, Fahad Al Debeyan (Lancaster University) and Lech Madeyski (Wroclaw University of Science and Technology). If you have any questions about the re-use of this survey, feel free to contact Fahad at <a href="mailto:e.winter@lancaster.ac.uk">f.aldebeyan@lancaster.ac.uk</a>.</p>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.