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10,006 results for “randomness”
Probing Rényi Entanglement Entropies via Randomized Measurements
<p>The data contained here is for the four figures in the main text of the manuscript Probing Rényi Entanglement Entropies via Randomized Measurements, which is available on the arXiv:<em>1806.05747. </em> The data is contained in tab delimited .csv files.</p>
Data for publication ''Interactive nutrition education is more effective in terms of improved levels of glycated hemoglobin in adolescent patients with poorly controlled type 1 diabetes – a randomized study''
<p>Data present changes in the most important clinical parameters after 3 and 6 months from the applied educational intervention in the group of children and adolescents with type 1 diabetes.</p>
Data for "A Random Walk" manuscript
<p>"GO D-G/" includes gene-annotation bipartite graph</p> <p>"LINCS D-G/" includes gene-drug bipartite graph</p> <p>"LINCS G-G/" includes gene-gene co-expression graph for LINCS cell line data</p> <p>"scRNA G-G/" includes gene-gene co-expression graph for single-cell data</p>
Data for publication on the impact of two different nutrition education methods on the quality of life of children and adolescents with type 1 diabetes (randomized study)
<p>Data present changes in the quality of life 6 months after the educational intervention applied in the group of children and adolescents with type 1 diabetes.</p>
Data for "Mobility enhancement in graphene by in situ reduction of random strain fluctuations"
<p>Data for "Mobility enhancement in graphene by in situ reduction of random strain fluctuations"</p>
Nests, threats and leks: non-random distribution of nests in ruffs (Calidris pugnax)
<p>This project investigates the nest distribution of the ruff (<em>Calidris pugnax</em>), an endangered shorebird, and its association with social and spatial characteristics of their habitat. Using satellite imaging, we established a suitable breeding area and analyzed the factors influencing nest placement. Our study aims to provide insights that can aid in the conservation of this species by understanding their nesting behavior and the threats they face.</p>
Effects of upper thoracic Mulligan mobilization on pain, range of motion and function in patients with mechanical neck pain: a randomized placebo-controlled trial
<p><span><span>Bu çalışmanın amacı mekanik boyun ağrısı olan bireylerde üst torasik bölgeye uygulanan Mulligan Ters Doğal Apofizeal Kaydırmaların (RNAGS) ağrı, hareket kısıtlılığı ve fonksiyon üzerindeki etkisini değerlendirmektir. Ocak 2021 ile Mayıs 2021 arasında yürütülen çalışmaya, sahte grup, fizyoterapi grubu ve Mulligan grubuna rastgele atanan 69 katılımcı dahil edildi. Tüm gruplara, paylaşımlı fizyoterapi müdahaleleri ve germe egzersizleri ile iki haftalık, 11 seanslık bir tedavi programı uygulandı. Mulligan grubuna Mulligan RNAGS tekniği ile ek mobilizasyon uygulandı. Sahte grup, standart altı Mulligan mobilizasyonu aldı. Sonuç ölçümleri arasında Hareket Aralığı (ROM), Görsel Analog Skala (VAS) ve Boyun Engellilik İndeksi (NDI) vardı. Gruplar arasında ölçümlerde hiçbir temel fark bulunamadı. Müdahaleden sonra, tüm gruplarda ROM artmış ve VAS ve NDI skorlarında azalma görüldü (p<0,001). Mulligan grubu diğer gruplara kıyasla VAS, ROM ve NDI'da önemli ölçüde daha fazla iyileşme gösterdi (p<0,001). Sham grubu fizyoterapi grubuna kıyasla NDI ve ekstansiyon ROM'da daha fazla iyileşme gösterdi (p<0,001). Üst torasik bölgedeki Mulligan RNAGS tekniği mekanik boyun ağrısında ağrı kesici, hareket aralığı ve işlevsellik açısından faydalı olduğu kanıtlandı. Uzun vadeli etkiler popülasyon bazlı çalışmalarla daha fazla araştırılmayı hak ediyor.</span></span></p>
Implementation and Results of a "Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment"
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) applied to the two-dimensional bin packing problem with and without orientation. As benchmark dataset, we use the <code>beng</code>, <code>A</code>, and <code>class</code> instances from <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> as well as the four non-trivial Almost Squares in Almost Squares (<a href="https://math.vu.nl/~sbhulai/publications/data_analytics2016b.pdf"><code>Asqas</code></a>) instances</p> <p>These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Rui Zhao, Zhize Wu, Daan van den Berg, Matthias Thürer, Tianyu Liang, Ming Tan, and Thomas Weise. Randomized Local Search for Two-Dimensional Bin Packing and a Negative Result for Frequency Fitness Assignment. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="https://site.unibo.it/operations-research/en/research/2dpacklib">2DPackLib</a> and other benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Rui ZHAO (赵睿) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:zr1329142665@163.com">zr1329142665@163.com</a>.</p>
Implementation and Results of a "Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem"
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA applied to the Quadratic Assignment Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Jiayang Chen, Zhize Wu, Sarah L. Thomson, and Thomas Weise. Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Jiayang CHEN (陈嘉阳) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:cjy65820607@163.com">cjy65820607@163.com</a>.</p>
Implementation and Results of a "Randomized Local Search vs. NSGA-II vs. Frequency Fitness Assignment on The Traveling Tournament Problem"
<p><strong><em>1. Introduction</em></strong></p> <p>In this archive, we provide the implementation and experimental results of a Randomized Local Search (RLS) with and without FFA as well as NSGA-II applied to the Traveling Tournament Problem. These are the data used in the paper below, which contains the exact specification of all algorithms, objective functions, and the encoding we applied.</p> <p>Cao Xiang, Zhize Wu, Daan van den Berg, and Thomas Weise. Randomized Local Search vs. NSGA-II vs. Frequency Fitness Assignment on The Traveling Tournament Problem. In <em>16th International Conference on Evolutionary Computation Theory and Applications (ECTA'24), part of the 16th International Joint Conference on Computational Intelligence (IJCCI'24)</em>. November 20-24, 2024. Porto, Portugal. Setúbal, Portugal: SciTePress.</p> <p>To run the experiments, you need <a href="https://thomasweise.github.io/moptipyapps">moptipyapps</a> and <a href="https://thomasweise.github.io/moptipy">moptipy</a>, which contain the actual algorithm implementations. Both packages are available on GitHub and on PyPI. However, we include several versions of them in the folder <code>source/packages</code>, just in case.</p> <p><strong><em>2. Directory Structure</em></strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>source</code> contains the Python source codes needed to run the experiment.</li> <li><code>source/packages</code> contains the source codes of the Python packages with the actual algorithm implementations.</li> <li><code>source/experiment_execution_scripts</code> contains the scripts to run the experiments.</li> <li><code>results</code> is the directory with the results, i.e., with the log files generated by the experiment.</li> <li><code>evaluator</code> is the folder containing Python scripts that were used to evaluate these results.</li> <li><code>evaluation</code> was generated using the evaluation scripts and contains tables and figures and a result summary in CSV format.</li> </ul> <p><strong><em>3. License</em></strong></p> <p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the benchmark datasets included, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file <code>LICENSE.txt</code> in this archive.</p> <p><strong><em>4. Contact</em></strong></p> <p>If you have any questions or suggestions, please contact</p> <p>Mr. Xiang CAO (曹翔) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥大学</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:452015026@qq.com">452015026@qq.com</a>.</p>
A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020
<p>The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.</p> <p> </p> <p> </p> <p>Variable: GPP, GPP<sub>sh</sub>, and GPP<sub>su</sub></p> <p>Spatial coverage: global</p> <p>Temporal coverage: 2002 to 2020</p> <p>Spatial resolution: 0.05×0.05°</p> <p>Temporal resolution: eight-day</p> <p>Unite: g C m<sup>−2</sup> d<sup>−1</sup></p> <p>Data format: raster (.tif)</p>
Replication Data for: Entanglement transition in random rod packings
<p>This repository includes the data and source code used to generate figures for </p> <p>Yeonsu Jung, Thomas Plumb-Reyes, Hao-Yu Greg Lin, L. Mahadevan<br>Entanglement transition in random rod packings.</p> <p>Please refer to the README.md file in each zip archive for a detailed description.</p> <ul> <li><strong>rod-packing-data-code.zip:</strong> Contains experimental and numerical data, along with source codes for plotting figures.</li> <li><strong>rod-packing-rod-segmentation-example.zip:</strong> Contains MATLAB functions and scripts demonstrating the image segmentation process.</li> </ul>
Data and code for "Tensor product random matrix theory"
<p>The data files and python scripts to generate Figure 3 of the manuscript "Tensor product random matrix theory" are uploaded.</p> <ul> <li>In the 'scripts' folder there are two python files. The file 'SFF.py' was used to generate the dataset. The file 'plot.py' generates Figure 3.</li> <li>In the 'processed data' folder there are .csv files with the data used to generate Figure 3.</li> <li>In the 'figures' folder the generated figure is included.</li> <li>In the 'additional plots' folder, three data sets and corresponding plots not incorporated in the paper are included.</li> </ul>
Transcutaneous spinal cord stimulation enhances motor score and gait recovery in incomplete spinal cord injury. A double-blind randomized controlled trial
<p><strong><span>Background: </span></strong><span>Although transcutaneous spinal cord stimulation (tSCS) has been suggested as a safe and feasible intervention for gait rehabilitation, no studies have determined its effectiveness compared to sham stimulation.</span></p> <p><strong><span>Objective: </span></strong><span>To determine the effectiveness of tSCS combined with robotic-assisted gait training (RAGT) on lower limb muscle strength and walking function in incomplete spinal cord injury (iSCI) participants.</span></p> <p><strong><span>Methods: </span></strong><span>A randomized, double-blind, sham-controlled clinical trial was designed. Twenty-seven subacute iSCI participants were randomly allocated to tSCS or sham-tSCS group. The intervention consisted of 20 sessions of standard Lokomat walking training enhanced with tSCS. Primary outcomes were the lower extremity motor score (LEMS) and dynamometry. Secondary outcomes included the 10-Meter Walk Test (10MWT), the Timed Up and Go test (TUG), the 6-Minute Walk test (6MWT), the Spinal Cord Independence Measure III (SCIM III) and the Walking Index for Spinal Cord Injury II (WISCI-II). Assessments were performed before and after the intervention and at 1-month follow-up.</span></p> <p><strong><span>Results: </span></strong><span>Although no significant differences between groups were detected after the intervention, the tSCS group showed greater effects than the sham-tSCS group for LEMS (3.4 points; p=0.033), 10MWT (37.5s; p=0.030), TUG (47.7s; p=0.009), and WISCI-II (3.4 points; p=0.023) at the 1-month follow-up. Furthermore, the percentage of subjects who were able to walk at the follow-up was greater in the tSCS group (85.7%) compared to the sham group (43.1%; p=0.029).</span></p> <p><strong><span>Conclusions:</span></strong><span> The combination of standard RAGT with tSCS for 20 sessions was effective for LEMS and gait recovery in subacute iSCI participants after one month of follow-up.</span></p> <p><strong><span>Key words: </span></strong><span>Spinal cord injury; Transcutaneous spinal cord stimulation; Lokomat; Robotic-assisted gait training; Motor function; </span><span>Gait rehabilitation.</span></p>
Multi-UAV Uniform Sweep Coverage in Unknown Environments: A Mergeable Nervous System (MNS)-Based Random Exploration
<p><strong>Abstract:</strong><br>This paper investigates the problem of multi-UAV uniform sweep coverage, where a homogeneous swarm of UAVs must collectively and evenly visit every portion of an unknown environment for a sampling task without having access to their own location and orientation. Random walk-based exploration strategies are practical for such a coverage scenario as they do not rely on localization and are easily implementable in robot swarms. We demonstrate that the Mergeable Nervous System (MNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in unknown environments by UAV swarms. To this end, we propose an MNS-based random walk approach where UAVs self-organize into a line formation using the MNS framework and then follow a random walk strategy to cover the environment while maintaining the formation. Through simulations, we test the efficiency of our approach against several decentralized random walk-based strategies as benchmarks. Our results show that the MNS-based random walk outperforms the benchmarks in terms of the time required to achieve full coverage and the coverage uniformity at that time, assessed across both the entire environment and within local regions.</p>
Data From: ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial
<p>Dataset Info</p> <p><strong>1) Immediate Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants’ iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in immediate test. </p> <p><strong>2) Delayed Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in delayed test. </p> <p><strong>3) Pre-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the responses of the participants to survey questions before the intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p> <p><strong>4) Post-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the evaluation of the participants to survey questions after intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p>
Inter-Chemical Correlation results for the study: HHEARx2017-2121 (The Dynamics of Exposure, Phthalates and Asthma in a Randomized Trial (DEPART))
Title: The Dynamics of Exposure, Phthalates and Asthma in a Randomized Trial (DEPART) <br>Species: Homo sapiens <br>Number of samples: 281 <br>Number of named analytes: 30 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=41 <br>
Exploring the Multidimensional Impact of Organizational Incentives on the Loyalty of Returned Overseas Faculty: An SEM and Random Forest Analysis
Open the record for dataset details and reuse information.
Verifiable measurement-based quantum random sampling with trapped ions
Open the record for dataset details and reuse information.
Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the 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.