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Fig. 10 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 10 – Two-dimensional temperature field of the "thick stone-soil" system for open space: A, time 10-00; B, time 14-00: Temperature scale in °C. The heights of rectangles are: the height above the sole (for the part above the dashed line) and the depth below the soil (for the part below the dashed line).
Fig. 7 in Characteristics of thermal processes in ant nests built under stones (Hymenoptera: Formicidae)
Fig. 7 – Relationship between the number of ants and their brood and the linear dimensions of the stone (average diameter, cm). A, workers in mid- June (R2 = 0.46788, p = 0.00088136); B, workers at the end of July (R2 = 0.27579, p = 0.017422); C, larvae at the end of May (R2 = 0.25457, p = 0.023286); D, pupae in mid-June (R2 = 0.16688, p = 0.073735).
Supplementary Datasets for: 'A processing and analytics system for microscopy data workflows: the Pycroscopy ecosystem of packages'
<p>The repository contains four independent datasets that are a part of the publication (<a href="https://arxiv.org/abs/2302.14629">arXiv:2302.14629</a>), which delineates the capabilities of the Pycroscopy ecosystem of packages. The details of the individual datasets can be found below. </p> <p>1) bfo_iv_final.hf5: Dataset of I-V curves captured by conductive atomic force microscopy on a BiFeO3 sample. The data has been transformed so that we plot not the log of the current density (J) as a function of the square root of the electric field. The dataset was originally presented in the paper 10.1038/s41467-017-01334-5 </p> <p>2) bto_atomic.dm3: Atomically resolved data BaTiO3 thin film acquired with scanning transmission electron microscopy. These were originally captured in the dm3 file format. This dataset was a part of the publication: doi.org/10.1002/adma.202106426</p> <p>3) EELS_STO.dm3: Scanning transmission electron microscope (STEM)-Electron energy loss spectroscopy (EELS) dataset of SrTiO3.</p> <p>4) STO-stack.h5: High-angle annular dark-field imaging (HAADF) scanning transmission electron microscope (STEM) image stack of SrTiO3. This image stack contains 25 images.</p> <p> </p>
Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.
<p>Dataset contains the data used for the quantitative analyses presented in the paper titled "Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.".</p> <p>Data were collected from a cross-sectional online survey of migrant women living in the Valencian Community of Spain between January and March 2023. The dataset contains demographic information about participants, as well as the barriers that the reported facing in the process of applying for residency within Spain.</p>
Microcoils manufacturing process H2020 UWIPOM2 PROJECT
<p>Microcoils manufacTuring process</p> <p>https://www.youtube.com/watch?v=gwXPUAHZKFk&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Miniaturized helical antenna manufacturing process - H2020 UWIPOM2 PROJECT
<p>Miniaturized helical antenna manufacturing process</p> <p>https://www.youtube.com/watch?v=Va-iSQlFMnY&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
Data for thesis titled: The impact of processing conditions on enzymatic protein hydrolysis performance from sardine (Sardina pilchardus) by-products using Alcalase 2.4L, and the influence on final spray dried hydrolysate powder properties
<p>The data answer the objectives that focused on:</p> <ol> <li>determining the substrate-specific optimum hydrolysis temperature and pH for the particular enzyme-substrate (Alcalase-sardine by-product) combination,</li> <li>investigating the effect of mixing speed, solids concentration and enzyme dosage on dry solids yield and protein recovery during enzymatic hydrolysis of sardine processing by-products,</li> <li>evaluating the influence of solids concentration on emulsion formation during enzymatic hydrolysis,</li> <li>determining the effect of solids concentration and emulsion formation on molecular weight distribution of protein hydrolysates,</li> <li>investigating the effect of mixing speed and solids concentration on the viscosity and mixing regime of material during enzymatic hydrolysis,</li> <li>establishing the role played by processing conditions (degree of hydrolysis (DH), maltodextrin addition and inlet air temperature) on powder recovery during spray drying, and</li> <li>investigating the role of DH, maltodextrin concentration and spray drying temperature on handling and storage properties of spray dried protein hydrolysates.</li> </ol> <p>This data also appears in journal papers with the following titles:</p> <p>Chiodza, K. & Goosen, N.J. 2023a. Evaluation of handling and storage stability of spray dried protein hydrolysates from sardine (Sardina pilchardus) processing by-products: Effect of enzymatic hydrolysis time, spray drying temperature and maltodextrin concentration. <em>Food and Bioproducts Processing</em>. (June, 30). DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0960308523000743?via%3Dihub">https://doi.org/10.1016/j.fbp.2023.06.009</a>.</p> <p>Chiodza, K. & Goosen, N.J. 2023b. Influence of mixing speed, solids concentration and enzyme dosage on dry solids yield and protein recovery during enzymatic hydrolysis of sardine (Sardina pilchardus) processing by-products using Alcalase 2.4L: a multivariable optimisation approach. <em>Biomass Conversion and Biorefinery</em>. 1:1–23. DOI: <a href="https://link.springer.com/article/10.1007/s13399-023-03829-2">https://doi.org/10.1007/s13399-023-03829-2</a>. </p> <p>Chiodza, K. & Goosen, N.J. 2023c. Emulsion formation during enzymatic protein hydrolysis and its effect on protein recovery and molecular weight distribution of protein hydrolysates from sardine (Sardina pilchardus) by-products. <em>Biomass Conversion and Biorefinery</em>. 1:1–12. DOI: <a href="https://link.springer.com/article/10.1007/s13399-023-04438-9">https://doi.org/10.1007/s13399-023-04438-9</a>.</p>
A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response
<p>Files containing additional data for the first and second open-ended response questions of the survey discussed in Section 3.3 of the paper "A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response" and text descriptions of the associated sonifications of three astronomical objects (the Galactic Center, Cassiopeia A, and the Chandra Deep Field South).</p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
Raw and Processed Data of Printed Markets - The Basel Avisblatt (1729-1845)
<p>Switzerland's first advertising journal – the so-called Avisblatt – was published in Basel. It is a typical example of a new market form emerging in 18th century Europe, yet it is also exceptional since all issues from 1729 to 1844/45 are preserved. In the SNF funded research project “Märkte auf Papier” at the University of Basel (PI: Prof. Dr. Susanna Burghartz), this serial source has been digitized and used to investigate economic, social and societal transformations of the early capitalist period in Basel. A database of all adverts and announcements was created, totalling 932’000 records and providing different sorts of metadata for each advert. The dataset is made avaiable combined with analytical tools.</p> <ul> <li>first official, public release</li> <li>raw (OCR'd) data, collections (data and meta data) and aggregated tables</li> <li>read and process conveniently with the avisblatt R package</li> </ul>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
Environmental Performance Assessment of a Novel Process Concept for Propanol Production from Widely Available and Wasted Methane Sources
<p>Dataset for associated publication.</p>
Geochronology and Geochemistry of Late Cenozoic Magmatism on Naozhou Island, Leiqiong Area: Implications for Deep Dynamic Processes
<p>The files of "Metadata 1" and "Metadata 2" are new measured data. The file of "References dataset" is published data from previous studies.</p>
Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds
<p>From a baby's babbling to a songbird practicing a new tune, exploration is critical to motor learning. A hallmark of exploration is the emergence of random walk behaviour along solution manifolds, where successive motor actions are not independent but rather become serially dependent. Such exploratory random walk behaviour is ubiquitous across species, neural firing, gait patterns, and reaching behaviour. Past work has suggested that exploratory random walk behaviour arises from an accumulation of movement variability and a lack of error-based corrections. Here we test a fundamentally different idea—that reinforcement-based processes regulate random walk behaviour to promote continual motor exploration to maximize success. Across three human-reaching experiments, we manipulated the size of both the visually displayed target and an unseen reward zone, as well as the probability of reinforcement feedback. Our empirical and modelling results parsimoniously support the notion that exploratory random walk behaviour emerges by utilizing knowledge of movement variability to update intended reach aim towards recently reinforced motor actions. This mechanism leads to active and continuous exploration of the solution manifold, currently thought by prominent theories to arise passively. The ability to continually explore muscle, joint, and task redundant solution manifolds is beneficial while acting in uncertain environments, during motor development, or when recovering from a neurological disorder to discover and learn new motor actions.</p>
Supplementary material for 'Reorganisation of subglacial drainage processes during rapid melting of the Fennoscandian Ice Sheet'
<p>Supplementary material for 'Reorganisation of subglacial drainage processes during rapid melting of the Fennoscandian Ice Sheet'</p> <p>This repository two zipped directories: Default_results_repository includes ISSM md (model) files, saved in the .mat MATLAB format for every model submission reported on in the corresponding manuscript. For the default model runs, the result .outbin files are also include for the ISSM ice sheet spinup, the GlaDS steady state run, and the GlaDS transient forcing run. For all other models runs, the included .mat files are executable such that running the included models will reproduce the rest of the results discussed in the paper.</p> <p>Manu_scripts includes the input geophysical data, shapefiles on Murtoo field locations from Ahokangas et al. (2021), glacial landforms shapefile data from Palmu et al. (2021), example input scripts used to produce the results discussed in the manuscript, and functions to plot those results are included. Note, the example scripts (A_ISSM_ice_spinup, B_GlaDS_seady_state, C_GlaDS_transient) will not run without modification to the specific users cluster and storage setup and are instead intended as guides to use for one's own model setup. </p> <p> For our modelling we used the Ice-sheet and Sea-level System Model (Larour et al., 2012) revision 27448 available from: https://issm.jpl.nasa.gov/ (last accessed on 06-09-2023). . </p> <p> </p>
Fig. 8. A-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 8. A-D − Nomarski (leftmost lane), FITC-immunofluorescence images labeled with anti-α-tubulin monoclonal antibody and their magnified images (middle two lanes), and red fluorescence images (rightmost lane) stained with Acti-stain 555 phalloidin (detection for F-actin) of encysting cells of C. cucullus Nag-1. Each set of photomicrographs arranged in a horizontal row shows an identical cell except for Fig. 8C (FITC image, inset). A − Vegetative cell. B-D − Encysting cells of C. cucullus Nag-1 at 1.5 h (B), 3 h (C) and 3 days (D) after encystment induction. E − Nomarski image (left), red fluorescence images (middle) stained with Acti-stain 555 phalloidin, and a Nomarski image superimposed with a red fluorescence image obtained by Acti-stain 555 phalloidin staining (right) in encysting cells of C. cucullus Nag-1 at 3 h after encystment induction. F − Silver impregnation of a 3-day-aged cyst showing the basal structure of cilia. This photograph was reproduced from our previous work (Watoh et al. 2005, Fig. 9b). ant: anterior end, le: lepidosome, mu: mucus layer, ec/en: ectocyst layer lined with endocyst layer, m: plasma membrane. B − arrowheads: swollen tip of cilia. C − arrowhead: oral apparatus.
Fig. 6 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 6. Ca2+/overpopulation-stimulated in vivo phosphorylation of p43 (actin, identified by MS) during resting cyst formation of C. cucullus Nag-1, detected by biotinylated Phos-tag/ECL assays (A), and blots stained with CBB after the biotinylated Phos-tag/ECL detection (B). Figures above the photographs indicate time lapse after onset of encystment induction.
Fig. 5 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 5. Photomicrographs (Nomarski images) (A) and transmission electron micrographs (B) of C. cucullus Nag-1 after onset of encystment induction, showing resorption of cilia. (A) Vegetative cell at 0 h (A-1) and 2.5 h (A-2) after onset of encystment induction. (B) Encysting 3-h-aged cell (B-1) and 4-h-aged cell (B-2). ci: cilia, m: plasma membrane, ec: ectocyst layer, le: lepidosome. (B-2) a different electron micrograph of the same ultrathin section used in a previous paper (Funatani et al. 2010; Fig. 3).
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.