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13,770 results for “performance”
Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)
<p>Acquired data necessary to perform the control algorithm introduced in this paper.</p> <p>a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps.<br> b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp.<br> c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.<br> </p> <p> </p>
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
<p>This archive includes the research data associated to the paper:</p> <p>Giuliano Casale. Accelerating Performance Inference over Closed Systems by Asymptotic Methods. Proc. ACM Meas. Anal. Comput. Syst., 1(1), 2017. The paper is accepted for presentation at ACM SIGMETRICS 2017.</p> <p>The research data requires MATLAB 2015a or later. Four datasets are included, each corresponding to a section of the paper:<br> - sec5.3.1: Small and medium models without infinite server nodes (Section 5.3.1)<br> - sec5.3.2: Large models without infinite server nodes (Section 5.3.2)<br> - sec5.3.3: Models with infinite server nodes (Section 5.3.3)<br> - sec5.4: Optimization programs (Section 5.4)</p> <p>A description of each dataset is included in the README.TXT file inside each folder.</p>
Low-entropy Packed Binary Detection using Hardware Performance Counters
<p><span>Malware analysis faces a critical challenge in accurately identifying packed executables, especially those with low entropy. Existing software-based solutions often fail in detecting packers used by malware, resulting in inaccurate classifications. To address this shortcoming, in this study we introduce a novel method using<br>Hardware Performance Counters (HPCs) to facilitate the classification of binary packers due to HPCs’ minimal access overhead and ability to obviate the necessity for source code. We trained classic machine-learning models by selecting relevant hardware attributes associated with the unpacking procedure for detecting<br>packers used by low-entropy binary programs. Extensive experiments shows the substantial role played by Hardware Performance Counters in detecting binary packing characterized by low entropy,<br>offering a promising avenue for further exploration and refinement of techniques in malware analysis<br><br><br></span></p> <p><span>The following zip files are executables that represent low entropy versions of software packers using byte-padding. The name of the files are the names of the packers which are represened, Acprotect, Armadillo, Aspack, Nspack, Pecompact, Petite, UPX, and Zprotect. These can be used to measure the unpacking process using hardware performance counters in order to test & train machine earning classifiers for accurate classification of low entropy packers.</span></p>
PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>
A diet containing mango peel silage impacts upon feed intake, energy supply and growth performances of dairy male calves
<p>The major challenges for disposal of waste from fruit processing factories are high transportation costs, limited landfill availability and environmental pollution. Therefore, developing efficient waste management techniques to reduce transportation costs and environment pollution is important. Mango peels (MP) are abundant during the mango season and high in fermentable carbohydrate, which can easily breakdown and pollute the environment if a proper waste management method is not implemented. Thus, in this study, fresh MP were ensiled after sun-dried for one day and then fed to dairy male calves as the roughage source to evaluate its effect on feed intake, digestibility, energy balance, body weight gain, feed efficiency and blood metabolites. Eight growing crossbred dairy male calves (Holstein Friesians × Zebu) were allocated into two groups [Control (n = 4) and mango peel silage (MPS, n = 4)]. This experiment lasted for 12 weeks and daily feed offered and refusal were recorded to determine the daily feed intake. Digestion trial was performed at the last five days of experiment. Body weight and measurement were recorded every two weeks interval to determine the weight gain and body physical improvement. Blood was collected at the end of experiment to analyze the serum biochemical parameters. Ensiling improved the energy and protein contents and decreased fibre content of MP, thereby improving the forage quality. Feeding MPS to calves increased (<i>P</i> < 0.05) feed intake, energy supply and energy balance, changes in body measurements, weight gain, feed efficiency, and glucose concentration, as well as lowered (<i>P</i> < 0.05) the urea nitrogen concentration. Ensiling fresh MP after sun-drying for one day improved silage quality, and feeding MPS to dairy male calves as a roughage source improved feed intake, energy supply and growth performances. Therefore, ensiling fresh MP could improve the feed supply for ruminant production and be an effective waste management strategy for fruit processing businesses. </p>
Comparing V2X and RADAR safety performance in NLOS scenarios
<p><strong>Scenario 1: </strong>Highway car following in road curve </p> <p>This scenario simulates a highway environment where two vehicles (HV and RV) communicate via V2X and HV is also equipped with radar sensor, while navigating a curved road. The leading remote vehicle (RV) is moving with constant speed and it is intially out of range of HV's radar sensor.</p> <p>Safety metrics such as Time-to-Collision (TTC) are evaluated to analyze the system's performance under the influence of NLOS situations and road curvature. <br><em>Dataset file: <code>Highway_road_curve_scenario.csv</code></em><br><br><strong>Scenario 2: </strong>Intersection scenario <br><br>This scenario involves two vehicles crossing each other paths and communicating via V2X at an intersection. Radar and V2X data are used to calculate safety indicators such as Time-to-Intersection (TTI), assessing the effectiveness of cooperative communication in mitigating collision risks. <br><em>Dataset file: <code>Intersection_scenario.csv</code></em></p>
Museum Webpages Performance Dataset | Ver 1
<p>This dataset thoroughly evaluates the web performance metrics for 234 museums worldwide. It includes 23 variables that assess technical performance, usability, and organizational details. The dataset contains information such as the organization’s name, domain, country, physical address, and the type of content management system (CMS) utilized. Performance metrics are available for both mobile and desktop platforms, addressing accessibility compliance, adherence to best practices, and search engine optimization (SEO). Specific metrics for mobile and desktop speed performance include First Contentful Paint (FCP), Total Blocking Time (TBT), Speed Index, Largest Contentful Paint (LCP), and Cumulative Layout Shift (CLS).</p> <p>Data collection was conducted using PageSpeed Insights. The CMS employed by each organization was identified through the WhatCMS tool. This dataset serves as a useful resource for researchers, web developers, and cultural institutions seeking to enhance digital inclusivity, optimize website performance, and benchmark their digital presence against global standards. It also provides a solid foundation for studying web optimization, accessibility, and the broader digital transformation of cultural heritage institutions.<br><br>It is noted that information regarding dataset's authors has been erased as the dataset is used in a research paper submitted to <em>Metrics </em>for peer review evaluation and potential publication<em>. </em></p>
Datasets: Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot
<h1>Documentation</h1> <p>This repository contains the measurement data presented in <strong>"Performance Characterization of Lithium-Ion Battery Cells Within Restricted Operating Range Using an Extended Ragone Plot."</strong></p> <p>The performance characterization was conducted on three lithium-ion battery cell types, each with two samples. In the publication only datasets from the following cells are included: #1: SCiB-23Ah-01, #2: SLPB8644143-353, #3: M1B-1223-01. The measurements were performed using a Scienlab SL60/300/18BT2C battery test system in combination with a BINDER type MK 720 temperature chamber. Further details about the battery cells, the experimental setup and procedures are available in the publication. An uncertainty analysis for these measurements is included in the supplementary material of the publication.</p> <blockquote> <p><strong>Note:</strong> Please cite the referenced publication when using these datasets in your work.</p> </blockquote> <h2>Datasets Overview</h2> <p><strong>Toshiba SCiB™ 23 Ah (prismatic)<br></strong></p> <ul> <li>SCiB-23Ah-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> <li>SCiB-23Ah-02 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (2.7 V); 2 (2.55 V); 3 (2.4 V); 4 (2.25 V); 5 (2.1 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>Shenzen Melasta Battery SLPB8644143 (pouch)<br></strong></p> <ul> <li>SLPB8644143-353<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> <li>SLPB8644143-45 <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (4.2 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 2 (4.1 V); 3 (4.0 V); 4 (3.9 V); 5 (3.8 V).</em></li> </ul> </li> </ul> </li> </ul> <p><strong>LithiumWerks (A123) ANR26650m1B (cylindrical)<br></strong></p> <ul> <li>M1B-1223-01<br> <ul> <li>OCVTest#1</li> <li>PowerTemperatureTest_dis-Umax#1 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 1 (3.6 V); 2 (3.45 V); 5 (3.3 V).</em></li> </ul> </li> <li>PowerTemperatureTest_dis-Umax#2<br> <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> <li> M1B-1223-02 <ul> <li>PowerTemperatureTest_dis-Umax#2 <ul> <li><em>This dataset contains the end-of-charge voltage variation levels i_U = 3 (3.4 V); 4 (3.35 V).</em></li> </ul> </li> </ul> </li> </ul> <h2>Usage instructions</h2> <ol> <li>Raw test reports are stored in the <code>test reports</code> directory as <code>*.csv</code> and <code>*.info.txt</code> files. These files contain the following measurement signals: <ul> <li>Time, Test step, ind_T, ind_U, ind_xP, E [J], Eneg [J], Epos [J], I [A], P [W], Q [As], Qneg [As], Qpos [As], T_1 [°C], T_2 [°C], T_3 [°C], T_Clima [°C], U [V]</li> </ul> </li> <li>The test reports are structured and consolidated into an HDF5 file (<code>Scienlab.h5</code>) for efficient storage and analysis. This HDF5 file can be processed using the <strong>HDF5 Data Analysis and Visualization Toolkit</strong>, provided in this repository. <ul> <li>The Python script <code>hdf5_main.py</code> is included for processing and visualizing the datasets.</li> <li>PowerTemperatureTest_dis-Umax datasets contain cyclization data from multiple constant power (CP) discharges and standardized constant current constant voltage (CCCV) charges, with varying end-of-charge voltages.</li> <li>OCVTest datasets contain low-current galvanostatic data required for performance characterization via reconstruction-based approaches. The test protocol is extensively described in the publication.</li> </ul> </li> </ol>
Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania
<p>These are the datasets underpinning the paper entitled: <strong>Long term effects of payment for performance on maternal and child health outcomes– evidence from Tanzania.</strong></p> <p><strong>The datasets are provided in csv and Stata 16 format along with variable descriptions, and the Stata do file used for analysis.</strong></p>
A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening [Feature Tables]
<p>This repository is supplementary to the manuscript "High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening" (DOI: 10.1021/acs.est.1c04168) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.</p> <p>Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>) for the annotated features. </p>
End-user's survey results on needs and expectations for next- generation Energy Performance Certificates (H2020 X-tendo project)
<p>The SPSS data file consists of survey data from the X-tendo project on the end-user needs and expectations from next-generation energy performance certificates.</p>
Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure (Data)
<p>Origin projects, figures and LabVIEW software used for the article "Performance of an Electrothermal MEMS Cantilever Resonator with Fano-Resonance Annoyance under Cigarette Smoke Exposure", published in <em>Sensors </em>on 14 Jun 2021.</p>
CBFdataset: A Dataset of Chinese Bamboo Flute Performances
<p><em>CBFdataset</em> is<em> </em>a dataset of Chinese bamboo flute (CBF) performances, created for ecologically valid analysis of music playing techniques in context.</p> <p>The dataset comprises monophonic recordings of classic CBF pieces and isolated playing techniques, recorded by 10 professional CBF performers; and expert annotations of seven playing techniques: vibrato, tremolo, trill, flutter-tongue (FT), acciaccatura, portamento, and glissando. The recorded pieces include <em>Busy Delivering Harvest (BH)</em> 扬鞭催马运粮忙, <em>Jolly Meeting (JM)</em> 喜相逢, <em>Morning (Mo)</em> 早晨, <em>and Flying Partridge (FP)</em> 鹧鸪飞. All data was recorded in a professional recording studio using a Zoom H6 recorder at 44.1kHz/24-bits. The difference between different Versions 1.2, 1.1, and 1.0:</p> <ul> <li>V1.2 is the complete CBFdataset with a total duration of 2.6 hours.</li> <li>V1.1 splits the CBFdataset into two subsets according to playing technique types: CBF-periDB and CBF-petsDB. The former contains all the full-length pieces, isolated playing techniques, and annotations of four periodic modulations: vibrato, tremolo, trill, and flutter-tongue. The latter comprises the same full-length recordings, isolated playing techniques, and annotations of three pitch evolution-based techniques: acciaccatura, portamento, and glissando.</li> <li>V1.0 includes only the CBF-periDB.</li> </ul> <p>Related updates, demos, and code for reproducibility are available at <a href="http://c4dm.eecs.qmul.ac.uk/CBFdataset.html">http://c4dm.eecs.qmul.ac.uk/CBFdataset.html</a>. Any queries, please feel free to contact Changhong at changhong.wang@telecom-paris.fr. Please cite the following paper when using this dataset:</p> <p>Changhong Wang, Emmanouil Benetos, Vincent Lostanlen, and Elaine Chew, "Adaptive Scattering Transforms for Playing Technique Recognition," <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP)</em>, 30 (2022): 1407-1421.</p>
Dataset of behavioral and neurophysiological data of a virtual sailing task published in: "Providing task instructions during motor training enhances performance and modulates attentional brain networks"
<p>Dataset belonging to the behavioral and neurophysiological data of the publication: "Providing task instructions during motor training enhances performance and modulates attentional brain networks". The two uploaded Zip files contain kinematic and electroencephalographic data of 36 participants for the Obstacle and HorizonTask.</p>
Data of "Decreasing the level of hemicelluloses in sow's lactation diet affects the milk composition and post-weaning performances of low birthweight piglets"
<p>This study showed the effects of decreasing the levels of hemicelluloses in sow’s lactation diet on milk composition and on sow and piglet performances.</p> <p>Sow_performances.csv: A comma separated value file of data used to investigate the relationship between the performances of Swiss Large White sows and decreasing levels of hemicelluloses in lactation diet.</p> <p>Milk_composition.csv: A comma separated value file of data used to investigate the relationship between the milk composition of Swiss Large White sows and decreasing levels of hemicelluloses in lactation diet.</p> <p>Piglets_performances.csv: A comma separated value file of data used to investigate the relationship between the performances of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>Feed_intake_piglets.csv: A comma separated value file of data used to investigate the relationship between the feed intake of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>Diarrhea_postweaning.csv: A comma separated value file of data used to investigate the relationship between the post-weaning diarrhoea of Swiss Large White pigs and decreasing levels of hemicelluloses in lactation maternal diet.</p> <p>data_description.xlsx: meta data for Sow_performances.csv, Milk_composition.csv, Piglets_performances.csv, Feed_intake_piglets.csv and Diarrhea_postweaning.csv with description of variables.</p> <p> </p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Snappable Meshes Performance Dataset
<p><strong>Overview</strong></p> <p>This dataset contains performance and navigation benchmarks obtained by generating eight maps with the snappable meshes algorithm multiple times. Data in columns is organized as follows:</p> <ul> <li><code>run</code> - Number of generation run.</li> <li><code>genset</code> - Generated map (maps "(a)" to "(h)" from the Benchmark scene.</li> <li><code>navset</code>- Number of navigation points used to generate the navigation metrics.</li> <li><code>tg</code> - Duration of the map generation process, in milliseconds.</li> <li><code>tv</code> - Duration of the map validation process, in milliseconds.</li> <li><code>c</code> - Average percentage of valid connections between navigation points.</li> <li><code>ar</code> - Relative area of the largest fully-connected (i.e., fully-navigable) region.</li> <li><code>nclu</code> - Number of isolated regions, i.e., of regions which are not connected to any another.</li> <li><code>genseed</code> - Seed used for the generation process.</li> <li><code>navseed</code> - Seed used for determining the validation metrics.</li> </ul> <p>A number of results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes" are obtained from this dataset.</p> <p><strong>Reproducibility of results</strong></p> <p>The results presented in the research paper "Procedural Generation of 3D Maps with Snappable Meshes", namely in Figure 8 and Figure 10, can be reproduced with the Jupyter notebook included with this dataset (file <code>analysis.ipynb</code>).</p> <p><strong>Licenses</strong></p> <p>The dataset is made available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license (see <code>LICENSE_DATA.txt</code>).</p> <p>The code in the Jupyter Notebook is made available under the <a href="https://opensource.org/licenses/MIT">MIT</a> license (see <code>LICENSE_CODE.txt</code>).</p>
Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power
<p>This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.</p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Machine learning code and dataset for "Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance"
<p>This repository contains the code and dataset for the paper:</p> <p>Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Natural Hazards and Earth System Sciences, 2022, <a href="https://doi.org/10.5194/nhess-2021-171">https://doi.org/10.5194/nhess-2021-171</a></p> <p>The GitHub code repository at <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">https://github.com/meteoswiss-mdr/ts-nowcast-datasources</a> may contain a more up-to-date version of the code if bug fixes etc. have been necessary. The file <a href="https://zenodo.org/api/files/41faa1b7-17f6-4a75-be09-7743426ef13c/ts-nowcast-datasources-publication.zip">ts-nowcast-datasources-publication.zip</a> in this Zenodo release contains the status of the GitHub repository at the time of the publication of the paper.</p> <p>For instructions for using the data, please see the <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">code repository</a>.</p> <p> </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.