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13,064 results for “predictions”
Computational Prediction and Experimental Realisation of Earth Abundant Transparent Conducting Oxide Ga-doped ZnSb2O6 - computational datasets
<p>Additional computational data for our publication on Ga-doped ZnSb2O6. This repository contains two files related to the defect calculations ("new-defect-corrections.json" and "aide-plotter"), which contain data concerning the charge correction schemes and total energies of all defect species, and the charge transition levels (which can be used to repeat the SCFL analysis), respectively. The other two files are the AMSET output files, which can be used to plot all the calculated charge transport data used in this study. Documentation for plotting using these files is provided at https://github.com/hackingmaterials/amset.</p> <p>Contact:</p> <p>joe.willis.15@ucl.ac.uk; d.scanlon@ucl.ac.uk</p>
Improving genome-wide association discovery and genomic prediction accuracy in biobank data
<p>Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ<sup>2</sup> value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies.</p>
The effects of temperature extremes on survival in two semi-arid Australian bird communities over three decades, with predictions to 2104
<p>Aim: Organisms in arid and semi-arid regions are frequently exposed to climatic extremes and accordingly among the most vulnerable to climate change. Studies of seasonal differences in vital rates, which mediate effects of climate on viability, are rare in arid species limiting ability to project population trends. We quantified survival patterns for two bird communities as a function of expo-sure to temperature extremes in winter and summer, then project survival patterns to 2104.</p> <p>Location: semi-arid eastern Australia</p> <p>Time period: 1986-2016; 1986-2104</p> <p>Major taxa studied: Birds</p> <p>Methods: Using mark recapture time-dependent Cormack-Jolly-Seber models and data for 37 species from two 30-year ringing programs, we tested for effects on 6-monthly survival of expo-sure to temperatures >38oC and <0oC. We then predicted future survival for different emission scenarios, testing whether changes in survival associated with warming winters would be suffi-cient to offset the effects of rising summer temperatures. Results: Survival probability declined strongly with increasing exposure to days >38oC and to a lesser extent to days <0oC, with temperature extremes explaining 43% and 13% of temporal varia-tion in survival among years, respectively. Summer survival patterns were similar across avian guilds but only survival of nectarivores declined in winter. Our models predict that gains in winter survival will not offset reductions in summer survival.</p> <p>Annual survival is predicted to decline sub-stantially by the end of the century: from 0.63 in 1986 to 0.43 in 2104 under an optimistic emis-sion scenario and to 0.11 under a pessimistic scenario. Main conclusions: We highlight the significance of temperature extremes for species' persistence in arid and semi-arid regions, comprising 70% of Australia's land mass, and 40% globally. Our demography-based results are consistent with physiological-based projections evaluating avian survival in arid and semi-arid regions globally and suggest rising summer temperatures pose a risk to population persistence in these regions. </p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources
<p>This directory contain the climate indices from CESM ensemble hindcasts during period 1900-2014 that are used for a manuscript ("Multidecadal Variation in the Seasonal Predictability of Winter PNA and Its Sources") submitted for Geophysical Research Letters.</p> <p>Description: </p> <p>The PNA index is defined by using the pointwise method , i.e., a linear combination of the normalized 500 hPa geopotential height anomalies (Z) at the four active centers: PNA = 1/4[Z(20°N, 160°W) – Z (45°N, 165°W) + Z (55°N, 115°W) – Z (30°N, 85°W)].The predicted seasonal mean PNA index is calculated based on the CESM monthly mean 500 hPa geopotential height prediction in December, January and February (DJF), i.e., with a 2-month lead time. The ensemble mean PNA index is the mean of 20 ensemble seasonal mean PNA indices. </p> <p>The predicted seasonal mean Niño 3.4 index is calculated as the area average of SSTA in the domain of 170°W-120°W, 5°S-5°N based on the CESM monthly mean SST in December, January and February (DJF), i.e., with a 2-month lead time. The linear signal of global warming is filtered out from the SSTA. The ensemble mean Niño index is the mean of 20 ensemble seasonal mean Niño 3.4 indices. </p> <p>The predicted seasonal mean PDO index is calculated as the time series associated with the first leading empirical orthogonal function (EOF) pattern of the winter mean (DJF) North Pacific SSTA (north of 20°N) based on the CESM monthly mean SST in December, January and February (DJF), i.e., with a 2-month lead time. The linear signal of global warming is filtered out from the SSTA. The ensemble mean PDO index is the mean of 20 ensemble seasonal mean PDO indices.<br> </p>
Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
<p><span><strong>Aim</strong>:</span><span> Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (<em>Ailuropoda melanoleuca</em>). </span></p> <p><span><strong>Location</strong>:</span><span> Sichuan Province, China.</span></p> <p><span><strong>Methods</strong>: </span><span>We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also</span><span> compared the differences in </span><span>distributional changes of giant pandas among the five mountains in the study area. </span></p> <p><span><strong>Results</strong>: </span><span>Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. </span></p> <p><span><strong>Main conclusions</strong>: </span><span>Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies. </span></p>
Remote sensing and field information aid in predicting the presence of the terrestrial orchid Cyclopogon lute-albus
<p>Tropical montane cloud forest is one of the most threatened ecosystems, in central Veracruz, Mexico. Within this ecosystem, terrestrial orchids are strongly dependent on forest conditions and may be sensitive to environmental change. We applied field surveys of abiotic and biotic factors associated with the presence of the terrestrial orchid <em>Cyclopogon luteo-albus</em> and combined this with correlative niche modeling approaches to evaluate its potential distribution under two different environmental sets and two different extents. Layers of environmental information were obtained from Landsat imagery and interpolated bioclimatic and soil property layers. Existing species records were used as training data by sampling five forest fragments in central Veracruz, utilizing herbarium and global biodiversity information facility databases. The resulting predictions were tested by sampling at 15 sites of potential distribution. The model predicted the presence of <em>C. luteo-albus</em> with a reliability of 80%. The most important variables of models derived from interpolated layers coincide with site-relevant parameters suggesting the utility of these tools to further explore species suitabilities at larger geographic extents. Potential distribution mapping is an important tool to identify key areas for conservation and priority areas for future studies of species and partially resolves the lack of records of many orchid species.</p>
A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations
<p>Supporting information dataset for <em>A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations, </em>including Flex ddG binding free energy predictions, epistasis calculations, pathway probabilities, Rosetta files and structural files. </p>
Towards future directions in data-integrative supervised prediction of human aging-related genes
<p><strong>Corresponding author:</strong> <a href="http://www.nd.edu/~tmilenko">Prof. Tijana Milenković</a>, tmilenko AT nd DOT edu.</p> <p><strong>Detailed descriptions of the data and program</strong>: check <a href="https://nd.edu/~cone/DirectionsAging/">our paper's website</a>.</p> <p><strong>Reference</strong>: Qi Li, Khalique Newaz, and Tijana Milenković (2022). <strong>Towards future directions in data-integrative supervised prediction of human aging-related genes.</strong><em> under review</em></p>
Insulin sensitivity in mesolimbic pathways predicts and improves with weight loss in older dieters
<p><span>Central insulin is involved in the regulation of hedonic feeding. Insulin resistance in overweight has recently been shown to reduce the inhibitory function of insulin in the human brain, but how this affects future weight management is unclear. Also unknown is the role of central insulin sensitivity on eating behavior of older people, who are highly vulnerable to hyperinsulinemia and in whom neural target systems of insulin action undergo age-related changes. Here, fifty overweight, pre-diabetic elderly participated in a double-blind, placebo-controlled pharmacological fMRI study before and after randomization to a 3-month caloric restriction or active waiting group. We show that treatment outcome in dieters can be predicted by baseline measures of individual intranasal insulin (INI) inhibition of value signals in the ventral tegmental area related to sweet food liking as well as, independently, by peripheral insulin sensitivity. At follow-up, both INI inhibition of hedonic value signals in the nucleus accumbens and whole-body insulin sensitivity improved with weight loss. These data highlight the critical role of central insulin function in mesolimbic systems for future weight management in humans and directly demonstrate that neural insulin function can be improved by weight loss even in older age, which may be essential for preventing metabolic disorders in later life.</span></p>
TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images
<p>This is the official repository for the paper TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images. We use the publicly available dataset from Taiwan University (Bai et al., 2019) as experimental data, consisting of four channels of TC satellite images with a temporal resolution of 3 hours, whose preprocessing method is also publicly available. We use infrared and passive microwave TC satellite image sequences for our experiments, each divided into 24-hour segments (8 infrared and 8 passive microwave satellite images, 16 in total), preprocessing and enhancing data as noted above, so there is no experimental error due to different data preprocessing methods. In this study, the 2003–2017 TC dataset from various global basins was divided into training (1097 TCs, 43528 events), validation (188 TCs, 7884 events), and test sets (94 TCs, 3196 events).</p>
PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2
<p>Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).</p>
Traits of soil bacteria predict plant responses to soil moisture
<p>Microbes can promote beneficial plant and animal responses to abiotic environments, but the ecological drivers of this benefit remain elusive. Here we investigated byproduct benefits, which occur when traits that increase the fitness of one species provide incidental benefits to another species with no direct cost to the provider species. In experimental mesocosms, microbial traits predicted plant responses to soil moisture such that bacteria with self-beneficial traits in drought increased plant early growth, size at reproduction, and chlorophyll concentration under drought, while bacteria with self-beneficial traits in well-watered environments increased these same plant traits in well-watered environments. Thus, microbial traits that promote microbial success in different soil moisture environments also promote plant success in these same environments. Our results show that the concept of byproduct benefits, originally conceived to explain the evolution of cooperation in pairwise mutualisms, also applies to interactions between plants and non-symbiotic soil microbes.</p>
Manually verified publication prediction dataset.
<p>Includes the clinical trial identifiers with publication status decisions sourced from three studies:</p> <p>- Joseph S Ross, Tony Tse, Deborah A Zarin, Hui Xu, Lei Zhou, and Harlan M Krumholz. Publication of NIH funded trials registered in clinicaltrials. gov: cross sectional analysis. BMJ, 344, 2012.</p> <p>- Adam G Dunn, Enrico Coiera, and Florence T Bourgeois. Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials. gov. Journal of clinical epidemiology 95: 94-101, 2018.</p> <p>- Deborah A Zarin, Tony Tse, Rebecca J Williams, Robert M Califf, and Nicholas C Ide. The ClinicalTrials. gov results database—update and key issues. New England Journal of Medicine 364, no. 9: 852-860, 2011.</p> <p>The authors of the studies have agreed with the publication of this dataset.<br> </p>
Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia
<p><span><strong>Background</strong>:</span><span> Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. Although the treatment landscape for CLL is rapidly evolving, there are still some patients who remain drug resistance or disease refractory. Ferroptosis is a type of lipid peroxidation-induced cell death and has been suggested with a prognostic value in several cancers. Our research aims to build a prognostic model to improve risk stratification in CLL patients and facilitate more accurate assessment for clinical management.</span></p> <p><span><strong>Methods</strong>: </span><span>The differentially expressed ferroptosis-related genes (</span><span>FRGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithms were performed to construct a prognostic risk model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were performed to estimate the immune infiltration score and immune-related pathways. A total of thirty-six CLL patients in our center were enrolled in this study as a validation cohort. Moreover, a nomogram model was established to predict the prognosis.</span></p> <p><span><strong>Results</strong>: </span><span>A total of differentially expressed 15 FRGs with prognostic significance were screened out. After minimizing the potential risk of overfitting, we constructed a novel ferroptosis-related prognostic score (FPS) model with nine FRGs (AKR1C3, BECN1, CAV1, CDKN2A, CXCL2, JDP2, SIRT1, SLC1A5 and SP1), and stratified patients into low-risk and high-risk groups. Kaplan–Meier analysis showed that patients with high FPS had worse overall survival (OS) (P<0.0001) and treatment-free survival (TFS) (P<0.0001). ROC curves evaluated the prognostic prediction ability of the FPS model. Additionally, the immune cell types and immune-related pathways were correlated with the risk scores in CLL patients. In the validation cohort, the results confirmed </span><span>that the </span><span>high</span><span>-</span><span>risk group was related to worse OS (P<0.0001), progress-free survival (PFS) (P=0.0140) and TFS (P=0.0072). </span><span>In</span><span> the multivariate analysis, only FPS (P=0.011) and CLL-IPI (P=0.010) were independent risk indicators for OS. Furthermore, we established a nomogram including FPS and CLL-IPI which could strongly and reliably predict individual prognosis.</span></p> <p><span><strong>Conclusion</strong>: </span><span>A novel FPS model could be used in CLL for prognostic prediction. The model index may also facilitate the development of new clinical ferroptosis-targeted therapies in patients with CLL.</span></p>
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses.
<p>The dataset to accompany the paper by Phoebe Chua, Dr. Dimos Makris, Prof. Dorien Herremans, Prof. Gemma Roig, and Prof. Kat Agres on Predicting emotion from music videos: exploring the relative contribution of visual and auditory information on affective responses.</p>
Maximally predictive states
<p>Datasets to reproduce the results from Costa et al. (2022) "Maximally predictive states: from partial observations to long timescales"</p>
Sars-escape network for escape prediction of SARS-COV-2
<p>This dataset belongs to a paper Sars-escape network for escape prediction of SARS-COV-2</p> <p><a>Prem Singh Bist</a>, <a>Hilal Tayara</a>, <a>Kil To Chong</a></p> <p><em>Briefings in Bioinformatics</em>, Volume 24, Issue 3, May 2023, bbad140, <a href="https://doi.org/10.1093/bib/bbad140">https://doi.org/10.1093/bib/bbad140</a></p> <p>Abstract</p> <p><strong>Motivation: </strong>Viruses have coevolved with their hosts for over millions of years and learned to escape the host's immune system. Although not all genetic changes in viruses are deleterious, some significant mutations lead to the escape of neutralizing antibodies and weaken the immune system, which increases infectivity and transmissibility, thereby impeding the development of antiviral drugs or vaccines. Accurate and reliable identification of viral escape mutational sequences could be a good indicator for therapeutic design. We developed a computational model that recognizes significant mutational sequences based on escape feature identification using natural language processing along with prior knowledge of experimentally validated escape mutants.</p> <p><strong>Results: </strong>Our machine learning-based computational approach can recognize the significant spike protein sequences of severe acute respiratory syndrome coronavirus 2 using sequence data alone. This modelling approach can be applied to other viruses, such as influenza, monkeypox and HIV using knowledge of escape mutants and relevant protein sequence datasets.</p> <p><strong>Availability: </strong>Complete source code and pre-trained models for escape prediction of severe acute respiratory syndrome coronavirus 2 protein sequences are available on Github at https://github.com/PremSinghBist/Sars-CoV-2-Escape-Model.git. </p> <p><strong>Contact: </strong>premsing212@jbnu.ac.kr.</p> <p><strong>Keywords: </strong>SARS-CoV-2; mutation; sequence analysis; viral escape prediction.</p>
Leaf traits predict water‐use eficiency in U.S. Pacific Northwest grasslands under rain exclusion treatment
<p>Does drought stress in temperate grasslands alter the relationship between plant structure and function? Here we report data from an experiment focusing on growth form and species traits that affect the critical functions of water‐ and nutrient‐use efficiency in prairie and pasture plant communities. A total of 139 individuals of 12 species (11 genera and four families) were sampled in replicated plots maintained for three years across a 520-km latitudinal gradient in the Pacific Northwest, USA. Rain exclusion did not alter the interspecific relationship between foliar traits and stoichiometry or intrinsic water‐use efficiency (iWUE). Rain exclusion reduced iWUE in grasses, and effect was primarily species‐specific, although leaf morphology, life history strategy, and phylogenetic distance predicted iWUE for all 12 species when analyzed together. Variation in specific leaf area explained most of the variation in iWUE between different functional groups, with annual forbs and annual grasses at opposite ends of the resource‐use spectrum. Our findings are consistent with expected trait‐driven tradeoffs between productivity and resource‐use efficiency and provide insight into strategies for the sustainable use and conservation of temperate grasslands.</p>
Genomic Prediction for the Germplasm Enhancement of Maize Project
<p>The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public and private sector maize breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multi-environment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on six years of topcross evaluations from the two GEM programs in Raleigh, NC and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from genomic selection ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within-program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm. </p>
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.