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The dataset and model code pertinent to the Everglades Peat Elevation Model (EvPEM): The salinity and inundation mesocosm experiment in freshwater and brackish water sawgrass wetlands in Florida Coastal Everglades (2015-2017).
This is an assembled data and Everglades Peat Elevation Model (EvPEMv1.0) Stella code used to estimate and simulate net ecosystem carbon balance (NECB) and peat elevation change in response to saltwater intrusion and level of inundations. Data from several studies were combined for the estimation of NECB, model parameterization, and calibration (Wilson, 2018; Wilson et al., 2018, 2019; Charles et al., 2019; Servais et al., 2020). The reported data includes aboveground net primary productivity (ANPP), belowground net primary productivity (BNPP), peat elevation change, and decomposition rates that were collected from outdoor laboratory mesocosm experiments conducted at the Florida Bay Interagency Science Center in Key Largo, Florida during 2015-17. The plant-soil monoliths were obtained from a freshwater peat and a brackish water peat marsh located within the Florida Coastal Everglades and transported to the Key Largo facility for the experimental manipulations. In experiments focused on the brackish water marsh, three experiments were carried out reflecting the combined effect of salinity, inundation, and peat exposure to air. The brackish water experiments characterized submerged (SUB), exposed (EXP), and extended depth of exposure of peat surface (EXTEXP) conditions, as we varied water depth relative to the peat surface. Each experiment was subjected to two salinity manipulations: (1) ambient (~10 ppt) porewater salinity (AMB) and (2) elevated (~20 ppt) salinity (SALT). The experimental design included six (2 X 3) treatments: (1) submerged ambient salinity (AMB.SUB), (2) submerged elevated salinity (SALT.SUB.), (3) exposed ambient salinity (AMB.EXP), (4) exposed elevated salinity (SALT.EXP), (5) exposed with extended exposure/dry-down ambient salinity (AMB.EXTEXP), and (6) exposed with extended exposure/dry-down elevated salinity (SALT.EXTEXP). The water level was kept 4 cm above the peat surface for the brackish water SUB treatments. Exposure for the EXP treatment
Data and code from Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest - 2021-2022
This dataset has been prepared in support of a paper to be published in Proceedings of the Royal Society B: Biological Sciences. It includes both data files and R scripts used for the analysis in this publication: Deitch, J.F. and S.A. Kaiser. 2023. Artificial light at night increases top-down pressure on caterpillars: experimental evidence from a light-naive forest. Proceedings of the Royal Society B: Biological Sciences. (https://doi.org/10.1098/rspb.2023.0153) Artificial light at night (ALAN) is a globally widespread and expanding form of anthropogenic change that impacts arthropod biodiversity. ALAN alters interspecific interactions between arthropods, including predation and parasitism. Despite their ecological importance as prey and hosts, the impact of ALAN on larval arthropod stages, such as caterpillars, is poorly understood. We examined the hypothesis that ALAN increases top-down pressure on caterpillars from arthropod predators and parasitoids. We experimentally illuminated study plots with moderate levels (10-15 lux) of LED lighting at light-naive Hubbard Brook Experimental Forest, New Hampshire. We measured and compared between experimental and control plots: 1) predation on clay caterpillars and 2) abundance of arthropod predators and parasitoids. We found that predation rates on clay caterpillars and abundance of arthropod predators and parasitoids were significantly higher on ALAN treatment plots relative to control plots. These results suggest that moderate levels of ALAN increases top-down pressure on caterpillars. We did not test mechanisms, but sampling data indicates that increased abundance of predators near lights may play a role. This study highlights the importance of examining the effects of ALAN on both adult and larval life stages and suggests potential consequences of ALAN on arthropod populations and communities. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubba
CODE-test: An annotated 12-lead ECG dataset
<pre># Annotated 12 lead ECG dataset Contain 827 ECG tracings from different patients, annotated by several cardiologists, residents and medical students. It is used as test set on the paper: "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. It contain annotations about 6 different ECGs abnormalities: - 1st degree AV block (1dAVb); - right bundle branch block (RBBB); - left bundle branch block (LBBB); - sinus bradycardia (SB); - atrial fibrillation (AF); and, - sinus tachycardia (ST). Companion python scripts are available in: https://github.com/antonior92/automatic-ecg-diagnosis -------- Citation ``` Ribeiro, A.H., Ribeiro, M.H., Paixão, G.M.M. et al. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun 11, 1760 (2020). https://doi.org/10.1038/s41467-020-15432-4 ``` Bibtex: ``` @article{ribeiro_automatic_2020, title = {Automatic Diagnosis of the 12-Lead {{ECG}} Using a Deep Neural Network}, author = {Ribeiro, Ant{\^o}nio H. and Ribeiro, Manoel Horta and Paix{\~a}o, Gabriela M. M. and Oliveira, Derick M. and Gomes, Paulo R. and Canazart, J{\'e}ssica A. and Ferreira, Milton P. S. and Andersson, Carl R. and Macfarlane, Peter W. and Meira Jr., Wagner and Sch{\"o}n, Thomas B. and Ribeiro, Antonio Luiz P.}, year = {2020}, volume = {11}, pages = {1760}, doi = {https://doi.org/10.1038/s41467-020-15432-4}, journal = {Nature Communications}, number = {1} } ``` ----- ## Folder content: - `ecg_tracings.hdf5`: The HDF5 file containing a single dataset named `tracings`. This dataset is a `(827, 4096, 12)` tensor. The first dimension correspond to the 827 different exams from different patients; the second dimension correspond to the 4096 signal samples; the third dimension to the 12 different leads of the ECG exams in the following order: `{DI, DII, DIII, AVR, AVL, AVF, V1, V2, V3, V4, V5, V6}`. The signals are sampled at 400 Hz. Some signals originally have a duration of 10 seconds (10 * 400 = 4000 samples) and others of 7 seconds (7 * 400 = 2800 samples). In order to make them all have the same size (4096 samples) we fill them with zeros on both sizes. For instance, for a 7 seconds ECG signal with 2800 samples we include 648 samples at the beginning and 648 samples at the end, yielding 4096 samples that are them saved in the hdf5 dataset. All signal are represented as floating point numbers at the scale 1e-4V: so it should be multiplied by 1000 in order to obtain the signals in V. In python, one can read this file using the following sequence: ```python import h5py with h5py.File(args.tracings, "r") as f: x = np.array(f['tracings']) ``` - The file `attributes.csv` contain basic patient attributes: sex (M or F) and age. It contain 827 lines (plus the header). The i-th tracing in `ecg_tracings.hdf5` correspond to the i-th line. - `annotations/`: folder containing annotations csv format. Each csv file contain 827 lines (plus the header). The i-th line correspond to the i-th tracing in `ecg_tracings.hdf5` correspond to the in all csv files. The csv files all have 6 columns `1dAVb, RBBB, LBBB, SB, AF, ST` corresponding to weather the annotator have detect the abnormality in the ECG (`=1`) or not (`=0`). 1. `cardiologist[1,2].csv` contain annotations from two different cardiologist. 2. `gold_standard.csv` gold standard annotation for this test dataset. When the cardiologist 1 and cardiologist 2 agree, the common diagnosis was considered as gold standard. In cases where there was any disagreement, a third senior specialist, aware of the annotations from the other two, decided the diagnosis. 3. `dnn.csv` prediction from the deep neural network described in the paper. THe threshold is set in such way it maximizes the F1 score. 4. `cardiology_residents.csv` annotations from two 4th year cardiology residents (each annotated half of the dataset). 5. `emergency_residents.csv` annotations from two 3rd year emergency residents (each annotated half of the dataset). 6. `medical_students.csv` annotations from two 5th year medical students (each annotated half of the dataset). </pre>
Data set and code supporting Marshall et al. 2020. No room to roam: King Cobras reduce movement in agriculture.
<p>Data and code used in the publication:</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. <em>Mov Ecol</em> <strong>8, </strong>33 (2020). https://doi.org/10.1186/s40462-020-00219-5</p> <p>Marshall, B.M., Crane, M., Silva, I., Strine, C.T., Jones, M.D., Hodges, C.W., Suwanwaree, P., Artchawakom, T., Waengsothorn, S., Goode, M. (2020). No room to roam: King Cobras reduce movement in agriculture. bioRxiv 2020.03.24.006676; doi: https://doi.org/10.1101/2020.03.24.006676</p> <p>Including: telemetry data, habitat shapefile and derived rasters, ISSF and JAGS model specification and results, code to reproduce analysis and generate figures. </p>
Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
<p>Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.</p>
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
<p>There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.</p>
Code for MATSim-NYC project
<p>This file includes the code and parameters for the baseline MATSim-NYC model, network calibration, and other additional features. All the required input files are saved in the input folder. </p> <p>The synthetic population as well as the data dictionary are also incorporated. </p> <p> </p>
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p> <p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p> <p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p> <p>Please find the code needed to replicate the main findings of the paper described below:</p> <ul> <li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li> <li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li> <li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li> <li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li> </ul> <p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
Data set and code supporting Marshall et al., "An inventory of online reptile images"
<p>Data set and code supporting: MARSHALL, B.M., FREED, P., VITT, L.J., BERNARDO, P., VOGEL, G., LOTZKAT, S., FRANZEN, M., HALLERMANN, J., SAGE, R.D., BUSH, B. and DUARTE, M.R., 2020. An inventory of online reptile images. <em>Zootaxa</em>, <em>4896</em>(2), pp.251-264. DOI:<a href="https://doi.org/10.11646/zootaxa.4896.2.6">10.11646/zootaxa.4896.2.6</a></p> <p>Data includes: </p> <ul> <li>Supplementary Table 1. List of all species and the number of photos in each of the 6 repositories: "SuppData1_Species_Photo_Count_Table_2020-08-04_no_syn.csv"</li> <li>Supplementary Table 2. List of species without photo in any of the 6 repositories: "SuppData2_Species_no_photos.csv"</li> <li>Supplementary Table 3. Per country summary data of number of species present and number with images: "SuppData3_Country_species_counts.csv"</li> <li>Reptile Database species checklist: "reptile_checklist_2020_04.csv"</li> <li>Reptile Database species synonyms used in second Wikimedia search: "reptile names 2019 syno.csv"</li> </ul> <p>Code includes:</p> <ul> <li>R code used to retrieve Flickr photograph metadata: "SuppCode1_Flickr_search.R"</li> <li>R code used to retrieve Wikimedia photograph metadata: "SuppCode2_Wikimedia_query.R"</li> <li>R code used to retrieve HerpMapper photograph metadata: "SuppCode3_HerpMapper_search.R"</li> <li>R code used to generate figures: "SuppCode4_Figure Generation.R"</li> </ul> <p>Also includes Zootaxa supplementary table.</p> <p> </p>
Code, data and scripts to study wave dynamics in asymmetric material
<p>Supplementary data for [1] Vladislav A. Yastrebov. "Wave propagation through an elastically-asymmetric architected material", 2021, https://arxiv.org/abs/1712.06294v2</p> <p>See "Readme.md" and indivual "Readme.md" files in folders: "data", "src", "fig"</p>
Data and R code for Tansley review New Phytologist 2021: "An integrated framework of plant form and function: The belowground perspective"
<p>The files in this archive are related to the paper of Weigelt, Mommer, Andraczek et al. (2021) An integrated framework of plant form and function: The belowground perspective. Tansley Review New Phytologist. The paper developed and tested a new conceptual framework of plant form and function linking above and belowground traits of 2510 species. We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow ‘conservation’ gradient on which leaf and fine-root traits were well aligned, and the ‘collaboration’ gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth.</p> <p>This archives contains four files:</p> <ol> <li><strong>Weigelt et al.2021RCode.DataCleaning.txt</strong> - RCode for the complete data processing starting with the downloaded database files from the Plant Trait Database version 5.0 (TRY, Kattge et al. 2020), the Global Root Trait database (GRooT, Guerrero-Ramirez et al. 2020) and a small number of additional data files listed in Table S2 of the original paper. Additional information was later incorporated using FungalRoot Database (Soudzilovkaia et al. 2020), nodDB Database (Tedersoo et al. 2018) and a compiled dataset on rooting depth (Fan et al. 2017). The code processes, cleans and merges the data and produces a final table for PCA analysis of species specific mean traits. This final table is provided as a second file in this archive (Weigelt_et_al_2021_Main.PCA.Matrix.xlsx). A second part of the RCode.DataCleaning extracts species-specific individual trait data where root and shoot traits were measured on the same plant individual or plot. This data was compiled from 43 studies identified in Table S2 of the original publication. The final table for individual trait data is the third file in this archive (Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx).</li> <li><strong>Weigelt_et_al_2021_Main.PCA.Matrix.xlsx</strong> – Datafile with species-specific global mean trait data for 17 traits of 2510 species with at least one root and one shoot trait available. Meta-data is provided in the data file.</li> <li><strong>Weigelt_et_al_2021_Individual.PCA.Matrix.xlsx</strong> – Datafile with species-specific trait data where root and shoot traits were measured on the same individual or plot for 6 traits of 455 species. Meta-data is provided in the data file.</li> <li><strong>Weigelt et al.2021RCode.Analysis.txt – </strong>RCode for all analyses and figures provided in the paper for both the species mean and individual based dataset. The Code is annotated to help reproducibility of the analysis.</li> </ol>
Diffraction images of crystals of the first and second spectrin repeats (mutant C420A/C435A) of human plectin (PDB code 2ODV): 2-wavelength SeMet MAD dataset
<p>Diffraction images of SeMet labeled crystals of a fragment of human plectin that includes the first and second spectrin repeats (SR1-SR2) of the plakin domain. The two Cys in the wild type sequence were replaced by Ala.</p> <p>This Se-Met MAD dataset was used for the <em>de novo</em> phasing of the pdb entry 2ODV (http://www.rcsb.org/pdb/explore/explore.do?structureId=2ODV).</p> <p> </p> <p>Data was collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a Mar CCD detector. Data from the same crystal were collected at two wavelengths :</p> <ul> <li>Remote wavelength (0.9185 Å): 180 images (1 degree oscillation per image).</li> <li>Peak wavelength ( 0.9785 Å): 360 images (1 degree oscillation per image).</li> </ul>
# Replication code and data for: Tracking green space along streets of world cities
<p># Replication code and data for: Tracking green space along streets of world cities<br>Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 </p> <p>The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains<strong> output data</strong>, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. </p> <p>____<br><br></p> <p>To replicate the analysis, the results, and the figures of the paper:</p> <ul> <li>Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking</li> <li><em>*Optional data extraction steps* </em>(processed output data are already available in the Zenodo repository):<br> <ul> <li>Adjust your working directory</li> <li>Run [lines 4-11] of workflow/sourcer.R</li> <li>Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files</li> </ul> </li> <li>Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)</li> </ul> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p dir="auto"> </p> <p dir="auto"> </p> </div> </div> </div> </div> </div>
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
Warragamba inflow data, predictors and code
<p>This repository contains data and code used for the analyses presented in Devanand et al. (2023) Can climate knowledge enable Warragamba Dam, Sydney, Australia to be used to manage flood risk?, 10.1088/1748-9326/ad0afb. The csv files contain historical inflow into the Warragamba dam in eastern Australia for the period 1970-2022, the large scale climate indices, and antecedent soil moisture. The climate predictors are obtained from https://psl.noaa.gov/gcos_wgsp/Timeseries/. The shape file 'warragamba_catchment.shp' is the catchment area that is used to estimate catchment average soil moisture from the AWRA-L datatset (https://dx.doi.org/10.25914/NDY0-J367). The python notebook 'probability_of_dam_inflows.ipynb' contains the code used to estimate inflow exceedance probabilities as a function of the predictors.</p>
Coding table for chapter 30 'Valency change and causation' in Bowern (ed. 2023)
<p>This is the coding table used for chapter 30 'Valency change and causation' (pp. 344-359) in the <i>Oxford Guide on Australian Languages </i>(Bowern, ed. 2023).</p>
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Data and Code for Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances
<p>Data and code for analyses in Lowman et al. 2024, Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances.</p> <p>See publication and ReadMe file for analysis description and further details. </p>
Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</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.