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zenodo44/100

Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)

<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p>&nbsp;</p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p>&nbsp;</p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. &nbsp;Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p>&nbsp;</p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. &nbsp;The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package &lsquo;metafor&rsquo;.</p> <p>&nbsp;</p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p>&nbsp;</p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>

opencc-by-4.0Apr 2024View details →
zenodo44/100

Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"

<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p>&nbsp; &nbsp; 1. &nbsp; &nbsp;<code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br>&nbsp; &nbsp; 2. &nbsp; &nbsp;<code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Remote Sensing Reflectance (Rrs)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Pure Water Absorption (aw)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Absorption Coefficient of Detritus (ad)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Total Absorption Coefficient without Pure Water (agp)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Absorption Coefficient of Phytoplankton (aph)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Backscattering Coefficient of Total Particulate Matter (bbp)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Scattering Coefficient of Total Particulate Matter (bp)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Chlorophyll a Concentration (Chl)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Inorganic Suspended Matter Concentration (ISM)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Single-Scattering Albedo of Detritus at 550 nm (A_d)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Power Law Exponent of Detritus Attenuation (G_d)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Water Salinity (Sal)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Water Temperature (Temp)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Fraction for Diminished Coccolithophore Absorption (a_frac)<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and R&ouml;ttgers (2023)</a><br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p>&nbsp; &nbsp; 1. &nbsp; &nbsp;OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology &amp; Oceanography, lno.12606. doi: 10.1002/lno.12606<br>&nbsp; &nbsp; 2. &nbsp; &nbsp;Component IOP Model: Bi, S., Hieronymi, M., and R&ouml;ttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br>&nbsp; &nbsp; 3. &nbsp; &nbsp;Pure Water IOP Model: R&ouml;ttgers, R., Doerffer, R., McKee, D., and Sch&ouml;nfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br>&nbsp; &nbsp; 4. &nbsp; &nbsp;Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Author: Shun Bi, Martin Hieronymi, R&uuml;diger R&ouml;ttgers<br>&nbsp; &nbsp; &bull; &nbsp; &nbsp;Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>

opencc-by-4.0Jul 2024View details →
zenodo44/100

Graph 4: Publication Types for Heiner Müller's Writings 1978-1988

<p>Graph 4 shows the publication types that Heiner M&uuml;ller used to publish his writing in the years from 1978-1988.</p>

opencc-by-4.0Mar 2023View details →
zenodo44/100

Graph 2: Publication Type and Venue of Heiner Müller's Writings

<p>This graph shows the different publication types and publication venues that Heiner M&uuml;ller used to publish his writings (essays, articles, speeches, etc.) from 1968 until 1998.</p>

opencc-by-4.0Mar 2023View details →
zenodo44/100

Graph 3: Publication Types for Heiner Müller's Writings 1967–1977

<p>Graph 3 shows the publication types that Heiner M&uuml;ller used to publish his writing in the years from 1967&ndash;1977.</p>

opencc-by-4.0Mar 2023View details →
zenodo44/100

Graph 5: Publication Types for Heiner Müller's Writings 1989-1998.

<p>Graph 5 shows the publication types that Heiner M&uuml;ller used to publish his writing in the years from 1989-1998.</p>

opencc-by-4.0Mar 2023View details →
zenodo44/100

Dataset Graph 2: Publication Type and Venue of Heiner Müller's Writings

<p>Dataset for Graph 2. Data collected based on the Heiner M&uuml;ller Werkausgabe.</p> <p>Graph 2 shows the different publication types and publication venues that Heiner M&uuml;ller used to publish his writings (essays, articles, speeches, etc.) from 1968 until 1998.</p>

opencc-by-4.0Mar 2023View details →
zenodo40/100

Dataset for the `Performance of the EUDET-type beam telescopes' publication (EPJ TI)

<p>This repository holds the data on which the publication `Performance of the EUDET-type beam telescopes' is based on.</p> <p>The substructure is as follows:</p> <p>Files in the range of run000060.raw until run000112.raw belong to a geometry with plane distance dz = 150 mm.<br> Files in the range of run000114.raw until run000171.raw belong to a geometry with plane distance dz = 20 mm.<br> A run corresponds to a doublet of beam energy and sensor threshold settings. The numbers are detailed in the README file.</p>

opencc-by-4.0Aug 2016View details →
zenodo40/100

Study of the failure mechanism of a HDPE liner in a Type IV high-pressure storage tank [Dataset related to publication]

<p>Data type: SEM micrographs; ATR-FTIR spectra; XRD patterns, DSC profiles and analyses. &nbsp;</p> <p>Data format: *.tif; *.dat; *.asc; *.opj.</p> <p>Origin of the data: laboratory equipment from University of Udine (SEM, ATR-FTIR, XRD, DSC).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>

opencc-by-4.0Feb 2024View details →
zenodo40/100

Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes

<p>Dataset of the publication: Spin-crossover tuning of the luminescence in 2D Hofmann-type compounds in bulk and exfoliated flakes</p> <p>DOI: 10.1039/d3tc03693f</p> <p>V. Garc&iacute;a-L&oacute;pez, F. Marques-Moros, J. Troya, J. Canet-Ferrer, M. Clemente. Le&oacute;n, E. Coronado</p> <p>J. Mater. Chem. C, 12, 161-169 (2024)</p>

opencc-by-4.0Nov 2024View details →
zenodo40/100

Data from the publication "The effect of moth trap type on size and composition in British Lepidoptera"

<p>Data from a study looking at differences in efficiency between three kinds of Robinson type light traps for catching moths. These data are the following: 1.&nbsp;Counts of moths of different families from three different trap types across six trapping nights. 2.&nbsp;Species level counts for the subset of the above defined as macromoths. 3.&nbsp;300-850nm electromagnetic spectra of all three traps made using a UV/visible spectrometer. 4.&nbsp;Weather data for the trapping period provided by the staff of Juniper Hall field centre.</p>

opencc-by-4.0Jun 2019View details →
zenodo40/100

Dataset supporting publication: Design Framework and Laboratory Experiments for Helix and Slinky Type Ground Source Heat Exchangers for Retrofitting Projects

<p>Dataset supporting publication: &ldquo;Design Framework and Laboratory Experiments for Helix and Slinky Type Ground Source Heat Exchangers for Retrofitting Projects&rdquo;&nbsp;(publication available for download:&nbsp;<a href="https://zenodo.org/record/7436458">GEOFIT Zenodo</a>).</p> <p>The focus of the experimental work was on shallow spiral geothermal heat exchanger configurations. Real-scale experiments were carried out for vertically oriented spiral collectors (helix) in sand and soil. One objective was to develop a measurement concept in laboratory environment to create a framework for a validated database. This database serves as the basis for further and new development of engineering design tools. To achieve the highest possible data-point density in the observed environment, temperature sensors and a fiber-optic temperature measurement system (DTS) were used. Soil probes were taken in situ before and after the measurements and analyzed at a thermophysical laboratory to determine material properties. The heat flow was controlled by an electric heating cable, which was installed in the form of a spiral-shaped heat exchanger in a 1 m&sup3; container. To guarantee constant boundary conditions, the measurements were carried out in a climate chamber at a defined ambient temperature. The evaluation of the transient response behavior is spatially resolved. The results are coordinate-based temperature points, which describe temperature gradients in all axes of the container over time, which are combined with known soil properties. The collected data was used to develop computational fluid dynamic (CFD) models, which are used to extend the variety of geometry and soil configurations for developing new design tools.</p>

opencc-by-4.0Dec 2022View details →
zenodo40/100

Dataset Graphs 3-5: Publication Types for Heiner Müller's Writings 1967–1977, 1978-1988, 1989-98.

<p>Dataset for Graphs 3-5. Data collected based on the Heiner M&uuml;ller Werkausgabe.</p> <p>Graphs 3-5 shows the publication types that Heiner M&uuml;ller used to publish his writing in the years from 1967&ndash;1977, 1978-1988, 1989-98.</p>

opencc-by-4.0Mar 2023View details →
zenodo36/100

Raw data for the publication "Effect of graphene substrate type on formation of Bi2Se3 nanoplates", Sci. Rep. 9, 4791, 2019. https://doi.org/10.1038/s41598-019-41178-1

<p>This dataset contains the raw AFM and SEM measurement data used for the study of stages of formation of Bi2Se3 nanoplates on different substrates and for the statistical analysis of sizes and thicknesses of the nanoplates. The results are published in&nbsp;Sci. Rep. 9, 4791, 2019. https://doi.org/10.1038/s41598-019-41178-1</p>

opencc-by-4.0Oct 2020View details →
zenodo36/100

2DVD dataset for GMD publication - Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign

<p>This archive contains the 2DVD measurement of graupel particles used in the GMD paper "Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign".</p><p>For each identified graupel particle, the following are included:</p><ul><li>Volume-equivalent diameter (mm)</li><li>Density (g cm-3)</li><li>Fall velocity (m s-1)</li></ul>

opencc-by-4.0Nov 2023View details →
zenodo36/100

Divergent Characteristics of Biomedical Research across Publication Types: A Quantitative Analysis on the Aging-related Research

<p>Attached in pdf here.</p>

opencc-by-4.0Dec 2023View details →
zenodo36/100

Metadata matrix: publication type (papers) by authors, from webQDA.

<p>Part of the thesis of Sonia Verdugo Castro (USAL, Spain).&nbsp;<br> Title of the article: &quot;The gender gap in STEM higher education studies: visualisation of literature&quot;.<br> Authors of the publication: Sonia Verdugo-Castro, M&ordf; Cruz S&aacute;nchez-G&oacute;mez, Alicia Garc&iacute;a-Holgado, Francisco J. Garc&iacute;a-Pe&ntilde;alvo</p>

opencc-by-4.0Oct 2021View details →
zenodo36/100

Metadata matrix: publication type by authors, from webQDA.

<p>Part of the thesis of Sonia Verdugo Castro (USAL, Spain).&nbsp;<br> Title of the article: &quot;The gender gap in STEM higher education studies: visualisation of literature&quot;.<br> Authors of the publication: Sonia Verdugo-Castro, M&ordf; Cruz S&aacute;nchez-G&oacute;mez, Alicia Garc&iacute;a-Holgado, Francisco J. Garc&iacute;a-Pe&ntilde;alvo</p>

opencc-by-4.0Oct 2021View details →
ClinicalTrials.gov36/100

Use of Continuous Glucose Monitors in Publicly-Insured Youth With Type 2 Diabetes - A Pilot and Feasibility Study

ClinicalTrials.gov study NCT05074667. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
dryad32/100

Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding

Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p&lt;0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p&lt;0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p&lt;0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p&lt;0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p&lt;0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.

opencc-zeroDec 2017View details →

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Last verified 2026-04-30Open record

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Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

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dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record