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1,211 results for “Instruments”
Evaluation of JavaScript Instrumentation by OpenTelemetry visualized in ExplorViz
<p>This dataset represents the results of a survey done with a small group, consisting mostly of experts in the research tool ExplorViz. The survey focused on evaluating whether the data that stem from automatic instrumentation by OpenTelemetry of JavaScript applications does benefit software comprehension. </p>
Dataset for publication "Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications"
<p>This is dataset related to paper published in 2020 IEEE I2MTC Conference proceedings:</p> <p>Crotti G., Giordano D., Letizia P. S., Delle Femine A., Gallo D., Landi C., Luiso M., Barbieri L., Mazza P., Pallidini D., (2020, October 29). Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications. https://doi.org/10.5281/zenodo.4154617</p> <p>Excel file contains the data for Figure 10 and following parameter evaluation.</p>
Data Set for Publication "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications"
<p>This is dataset for paper published in CPEM2020 Conference Proceedings:</p> <p>Crotti G., Delle Femine A., Gallo D., Giordano D., Landi C., Letizia P.,S., Luiso M., "Traceable Characterization of Low Power Voltage Instrument Transformers for PQ and PMU Applications".</p> <p>https://doi.org/10.5281/zenodo.4153819</p> <p>Excel file provides data for Figure 2 and following evaluation</p> <p> </p>
Dataset: A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern
<p>NORMAN Suspect List Exchange was used for the generation of the dataset. Datasets with clear label (LC or GC) were used. More specifically, we used S3 NORMANCT15, which contains a list of compounds that were detected in surface water from the Danube River in a pan-European collaborative trial employing both GC-HRMS and LC-HRMS. Moreover, the GC and LC target list were used by the following two institutes: National and Kapodistrian University of Athens (NKUA) and Helmholtz Centre for Environmental Research (UFZ). S21 UATHTARGETS is the LC target list of NKUA, S65 UATHTARGETSGC is the GC target list of NKUA and S53 UFZWANATARG contains the LC and GC target list of UFZ. Finally, two GC target lists (S51 WRIGCHRMS and S70 EISUSGCEIMS) were used. These lists contain GC substance lists and were provided by two Slovak institutes, the Water Research Institute (WRI) and Environmental Institute. The aforementioned compound lists were merged together to form a labelled dataset. The SMILES were used to calculate 1446 molecular descriptors. 1446 descriptors were produced by PaDEL-descriptor, logP was produced by JRgui and boiling point by USEPA ECOSAR.</p> <p>The dataset is used in the publication:</p> <p>"A multi-label classifier for predicting the most appropriate instrumental method for the analysis of contaminants of emerging concern" authored by</p> <p>Nikiforos Alygizakis, Vasileios Konstantakos, Grigoris Bouziotopoulos , Evangelos Kormentzas, Jaroslav Slobodnik and Nikolaos S. Thomaidis</p> <p>Github repository: https://github.com/nalygizakis/LCvsGC</p>
Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also <strong>f</strong>rom satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles</p>
Ethylene (C2H4) point-sources detected by the IASI infrared satellite instrument (2008-2020).
<p>This dataset includes the super-sampled IASI 0.01° × 0.01° C<sub>2</sub>H<sub>4</sub> HRI dataset in GeoTIFF format and the catalogue of the identified and categorized C<sub>2</sub>H<sub>4</sub> point-sources in kml format (C2H4_HRI_pointsources.zip). It also includes the super-sampled IASI C<sub>2</sub>H<sub>4</sub> total columns used to calculate the emission fluxes from the analyzed point-sources (C2H4_column_pointsources.zip) and the source data needed to reproduce the figures (C2H4_SourceData.zip). The code to calculate the C<sub>2</sub>H<sub>4</sub> HRI from IASI spectra and to retrieve the C<sub>2</sub>H<sub>4</sub> total columns is provided (C2H4_codes.zip), along with the artificial neural network used for the retrievals, instructions and example data. The codes of the oversampling, wind rotation and supersampling are available in the paper of Clarisse <em>et al.</em> (2019) at <a href="https://doi.org/10.5194/amt-12-5457-2019">https://doi.org/10.5194/amt-12-5457-2019</a>.</p>
Reference atmospheres, surface and instrument data used in the test experiments presented in Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82)
<p>The supplied archive includes a set of ASCII files defining the reference atmospheric and surface states that are the basis of the simulation experiments presented in the paper of Ridolfi et al. 2022 (doi.org/10.5194/amt-2022-82). Along with atmospheric and surface data, we also supply the seasonal variability and mismatch errors used in the test experiments presented in that paper, the noise error covariance matrices anticipated for FORUM and IASI-NG spectra, and some details of the retrieval setup. The archive includes a README file summarizing the contents of the various files provided.</p>
Dataset: Veeco Instruments Inc. (VECO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Texas Instruments Incorporated (TXN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: MKS Instruments, Inc. (MKSI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE) Part I: Instrument description and level 1 radiances (data set)
<p>The data set uploaded to this repository is outlined in a manuscript submitted to the journal Atmospheric Measurement Techniques.</p> <p>A. BB_effective_emissivity: Holds the data set used to characterise instrument calibration target emissivity</p> <p>B. ILS_data: Holds the data set used to model the instrument spectral lineshape</p> <p>C. Time_resolved_spectral_response: Holds the data set establishing the spectral stability of the instrument</p> <p>D. Zenith_radiances_20220323_1100UTC: Holds calibrated radiances acquired by FINESSE to demonstrate it accuracy and precision. Also included in this file is an LBLRTM simulation using coincident ERA5 profile information for the time and location of the observations</p> <p>The Far INfrarEd Spectrometer for Surface Emissivity (FINESSE). Part I: Instrument description and level 1 radiances</p> <p>Jonathan E. Murray1,2, Laura Warwick3, Helen Brindley1,2, Alan Last1, Patrick Quigley1, Andy Rochester1, Alexander. Dewar1, Daniel. Cummins1</p> <p>1 Department of Physics, Imperial College London, SW7 2BX, UK</p> <p>2 National Centre for Earth Observation, UK</p> <p>3 ESA-ESTEC, Noordwijk, Netherlands</p> <p>In the manuscript Part (I) we describe the FINESSE system configuration, outlining the FINESSE spectral characteristics, the data acquisition methodology and the calibration strategy. As part of the process, we evaluate the stability of the system, including the impact of knowledge of blackbody target emissivity and temperature. We also establish a numerical description of the instrument line shape. We demonstrate why it is important to account for these effects by assessing their impact on the overall uncertainty budget on the level 1 radiance products from FINESSE.</p>
Musical instrument playing with the Parametric hand data
<p>Data collected and source files for generation. Instructions on use and links to resources in README.txt</p> <p>Robot trajectories, logs, waveforms and loadcell data for experiments in embodied intelligence during music playing with the Parametric robot hand.</p>
Beijing Opera Percussion Instrument Dataset
<p>The Beijing Opera percussion instrument dataset is a collection of audio examples of individual strokes spanning the four percussion instrument classes used in Beijing Opera (Jingju, 京剧).</p> <p>Beijing Opera uses six main percussion instruments that can be grouped into four classes: </p> <ol> <li><strong>Bangu</strong> (Clapper-drum) consisting of Ban (the clapper, a wooden board-­shaped instrument) + danpigu (a wooden drum struck by two wooden sticks)</li> <li><strong>Naobo</strong> (Cymbals) consisting of two cymbal instruments Qibo+Danao</li> <li><strong>Daluo</strong>: Large gong</li> <li><strong>Xiaoluo</strong>: Small gong</li> </ol> <p><strong>Audio content</strong></p> <p>The dataset provides audio examples for each of these instrument classes.</p> <p>The audio examples were recorded under studio conditions by Mi Tian at the <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The instruments were played by Ying Wan of the London Jing Kun Opera Association. Unlike some instruments that can be tuned, these percussion instruments are made from metal casting. Thus, there can be subtle timbral differences even across different instruments of the same kind. For each of these instruments, we used 2-3 individual instruments to record the samples, hoping to achieve a better timbre coverage. Further, audio samples were recorded using different playing techniques for each instrument.</p> <p>The dataset can be used for training models for each percussion instrument class. </p> <p>Each audio file is named as, </p> <pre><code><InstrumentClass>_<InstanceNumber>.wav</code></pre> <p><strong>Using this dataset</strong></p> <p>Please cite the following paper if you use this dataset in your work:</p> <blockquote> <p>Mi Tian, Ajay Srinivasamurthy, Mark Sandler, and Xavier Serra, "A Study of Instrument-wise Onset Detection in Beijing Opera Percussion Ensembles", in Proceedings of ICASSP 2014, Florence, Italy, May 2014.</p> </blockquote> <p><a href="https://doi.org/10.1109/ICASSP.2014.6853981">https://doi.org/10.1109/ICASSP.2014.6853981</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us: </p> <p>Mi Tian ( m.tian@qmul.ac.uk ) or Ajay Srinivasamurthy ( ajays.murthy@upf.edu)</p> <p> </p> <p><a href="http://compmusic.upf.edu/bo-perc-dataset">http://compmusic.upf.edu/bo-perc-dataset</a></p>
PsPM-PIT2 : PSR, SCR, ECG and respiration measurements from pavlovian to instrumental transfer tasks with visual CS and electrical US.
<p>This dataset consists of a three session experiment conducted with 38 healthy unmedicated participants (22 females and 16 males aged 24.8 +/- 3.6). The sessions were recorded on the same day with a self decided break. The first session is an instrumental conditioning, the second a classical (Pavlovian) discriminant delay fear conditioning and the third a Pavlovian-to-instrumental transfer task. For all three sessions this dataset contains electrocardyogramm (ECG), pupil size (PSR), respiration, and skin conductance (SCR) measurements. For the fear conditioning session CS+/CS- are visual stimuli of differently colored backgrounds. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.5 s. ITI is randomly determined on each trial to be an integer between 7 - 11 s. The instrumental stimuli are two images of differently colored vending machines cuing if the machine required a coin to dispense a chocolate (Approach) or if a coin should be averted from the machine, to prevent an already dispensed chocolate from being crushed by a newly dispensed soft drink can (Withdraw). The operant task asks subjects to repeatedly press the space key (Go) or withhold any key press (NoGo) to win chocolates. In the instrumental conditioning session, subjects play the game with grey background, and in the Pavlovian-to-instrumental transfer session they play the same operant game but with CS colored backgrounds instead of grey background.</p>
PsPM-PIT1 : PSR, SCR, ECG and respiration measurements from pavlovian to instrumental transfer tasks with visual CS and electrical US.
<p>This dataset consists of a three session experiment conducted with 22 healthy unmedicated participants (13 females and 9 males aged 26.2 +/- 3.6). The sessions were recorded on the same day with a self decided break. The first session is an instrumental conditioning, the second a classical (Pavlovian) discriminant delay fear conditioning and the third a Pavlovian-to-instrumental transfer task. For all three sessions this dataset contains electrocardiogram (ECG), pupil size (PSR), respiration and skin conductance (SCR) measurements. For the fear conditioning session CS+/CS- are visual stimuli of differently colored backgrounds. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.0 s. ITI is 2.5 s. The instrumental stimuli are two images of differently colored vending machines cuing if the machine required a coin to dispense a chocolate (Approach) or if a coin should be averted from the machine, to prevent an already dispensed chocolate from being crushed by a newly dispensed soft drink can (Withdraw). The operant task asks subjects to repeatedly press the space key (Go) or withhold any key press (NoGo) to earn chocolates. In the instrumental conditioning session, subjects play the game with grey background, and in the Pavlovian-to-instrumental transfer session they play the same operant game but with CS colored backgrounds instead of grey background.</p>
VGAM: Compact and Low Power Mass Spectrometer-based Instrumentation for Volcanic Gas Monitoring
<p>Compact mass spectrometers can provide simultaneous multi-species analysis with high sensitivity and precision.<br> For volcanic gas monitoring in situ, instrumental mass spectrometry requires reliability and ruggedness combined<br> with low power usage and portability. The Volcanic Gas Analytical Monitor (VGAM) is capable of quantitative<br> molecular analysis of a variety of atmospheric and volcanic gases in a single sensor by ion trap mass<br> spectrometry. These gases include: H 2 , He, H 2 O, N 2 , O 2 , Ar, NO, N 2 O, CO, CO 2 , H 2 S, SO, SO 2 , and CH 4 . Unlike<br> previous field instruments using magnetic sector and quadrupole mass spectrometers (MS) with vacuums backed<br> by compact turbomolecular pumps, the VGAM uses a low-power autoresonant ion trap MS and NEG-Ion vacuum<br> that operate at only 25 W total power, which is often the power requirement for just the mass spectrometer. Ratio-<br> metric mass spectral response is combined with total pressure measurements to report absolute partial<br> pressures. Data are generated in real time and are recorded to internal flash memory. Relatively low power (&lt;1<br> Watt) data telemetry to remote sites is possible. Analysis of volcanic plumes, fumaroles, and solfatara fields is<br> accomplished by direct inlet of gases, after trapping as much excess water vapor in both the external and internal<br> foreline as possible. We report herein on the VGAM auto-run and post-processing procedures, initial calibration<br> results for CO 2 , and the results of a brief field deployment at Sulphur Banks solfatara field, Kilauea Volcano,<br> Hawaii.</p>
A National Forum on Web Privacy and Web Analytics — Participant Survey Instrument
<p>This survey instrument was administered to participants of the <em>National Forum on Web Privacy and Web Analytics</em>. Results informed the Forum event and Forum deliverables.</p> <p>The <em>National Forum on Web Privacy and Web Analytics </em>was held September 2018 in Bozeman, Montana, where 40 librarians, technologists, and privacy researchers collaborated in producing a practical roadmap for enhancing our analytics practice in support of privacy.</p> <p>More information is available on our project site: <a href="https://osf.io/gnfpu/">https://osf.io/gnfpu/</a>. </p> <p>This project is made possible in part by the Institute of Museum and Library Services, through grant <a href="https://www.imls.gov/grants/awarded/lg-73-18-0100-18"># LG-73-18-0100-18</a>.</p>
List of 10-most funded projects from the Instrument contributing to Stability and Peace 2018
<p>List of 10-most funded projects from the Instrument contributing to Stability and Peace 2018 - This is a dataset from the research financed by the research and innovation programme Horizon 2020 of the European Union in light of the grant agreement Marie Sklodowska-Curie No 660933 - PARADOXGREATLAKES.</p>
Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results
<p><strong>Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results </strong></p> <p> </p> <p>This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019. [1, 2]. The data files contents and format are described.</p> <p> </p> <p>Each data file is stored in HDF5 format written with h5py [3, 4].</p> <p>The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure:</p> <blockquote> <p>+</p> <p>:|____Author (str [50])</p> <p>:|____Bias (str [3])</p> <p>:|____DOI (str [22])</p> <p>:|____Date (str [14])</p> <p>:|____Description (str [81])</p> <p>:|____File_Creator (str [44])</p> <p>:|____License (str [2140])</p> <p>|____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))</p> <p> :|____Description (str [41])</p> <p> :|____Format (str [94])</p> <p> :|____Units (str [3])</p> <p>|____XYZ (h5py._hl.dataset.Dataset (3, 101))</p> <p>|____phi (h5py._hl.dataset.Dataset (101, 101, 101))</p> <p> </p> </blockquote> <p>See the metadata within the file for units of each variable.</p> <p> </p> <p>The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure:</p> <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [32])</pre> <pre> :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [59])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [44])</pre> <pre> :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre> :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [47])</pre> <pre> :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [37])</pre> <pre> :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre> :|____Description (str [38])</pre> <pre> :|____Units (str [4])</pre> </blockquote> <p> </p> <p>The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.</p> <p> </p> <p>1. Delzanno, G.L., et al., <em>CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies.</em> IEEE Transactions on Plasma Science, 2013. <strong>41</strong>(12): p. 3577-3587.</p> <p>2. Meierbachtol, C.S., et al., <em>An electrostatic Particle-In-Cell code on multi-block structured meshes.</em> Journal of Computational Physics, 2017. <strong>350</strong>: p. 796-823.</p> <p>3. Folk, M., A. Cheng, and K. Yates. <em>HDF5: A file format and I/O library for high performance computing applications</em>. in <em>Proceedings of supercomputing</em>. 1999.</p> <p>4. Collette, A., et al., <em>h5py/h5py 2.9.0.</em> 2018.</p> <p> </p> <p> </p>
Raw and processed data, gating strategy, and photographs of instrumental setup of NAVETTA
<p>Supplementary Information and Raw Data for Weiss et al., Comp Struct Biotechn J: Nanosci Adv Mat, 2024</p> <p>1. xls sheet of raw and processed data for all figures</p> <p>2.-3. gating strategies for flow cytometry experiments</p> <p>4.-8. photographs of instrumental setups</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.