Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
863
datasets available to search
ShareScore release 0.9.0
Dataset results
863 results for “metabolism studies”
Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007
Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation:Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
Integration of sweet taste and metabolism determines carbohydrate reward-study 3
Open the record for dataset details and reuse information.
Inter-Chemical Correlation results for the study: HHEARx2017-1977 (Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density.)
Title: Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density. <br>Species: Homo sapiens <br>Number of samples: 1116 <br>Number of named analytes: 41 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=46 <br>
Supplementary material for "Learning Boolean controls in regulated metabolic networks: a case-study"
<p>This record contains notebooks and Docker image for reproducing the learning of Boolean controls in regulated metabolic networks, and the case study presented in the CMSB 2021 conference proceeding article "Learning Boolean controls in regulated metabolic networks: a case-study".</p> <p>Visualize notebooks online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Simulations.ipynb">CaseStudy-Simulations.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-SearchSpace.ipynb">CaseStudy-SearchSpace.ipynb</a></li> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/5060985/files/CaseStudy-Inference.ipynb">CaseStudy-Inference.ipynb</a></li> </ul> <p>Notebooks can be executed interactively within the Docker image <code>bioasp/boolean-caspo-flux:cmsb2021 </code>which extends the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> version <code>2021-02-01.</code></p> <p>Alternatively, they can be executed without any installation at <a href="https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/">https://mybinder.org/v2/zenodo/10.5281/zenodo.5070151/</a>.</p> <p>The Docker image can be executed as follows:</p> <pre><code class="language-bash">docker pull bioasp/boolean-caspo-flux:cmsb2021 docker run -it --rm -p 8888:8888 bioasp/boolean-caspo-flux:cmsb2021 </code></pre> <p>then point your browser to <a href="http://127.0.0.1:8888">http://127.0.0.1:8888</a>.</p> <p>The image can be imported using the command <code>docker load</code> with the image file provided in this record:</p> <pre><code>docker load -i image.tar.gz</code></pre> <p>or with the <code>donodo</code> command available at <a href="https://github.com/pauleve/donodo">https://github.com/pauleve/donodo</a>:</p> <pre><code>pip install -U donodo donodo pull 10.5281/zenodo.5070151</code></pre>
Data for the study of Posidonia microbial nitrogen metabolism manuscript
<p>These are data related to the study of the seagrass microbiome and function. <em>Posidonia oceanica</em>, an endemic seagrass species that dominates the Mediterranean Sea, achieves high abundances in seawater with relatively low concentrations of dissolved inorganic nitrogen. Here we test whether microbial metabolisms associated with <em>P. oceanica</em> and surrounding seawater enhance seagrass access to nitrogen. Using stable isotope enrichments of intact seagrass with amino acids, we show that ammonification by free-living and seagrass-associated microbes produce ammonium that is likely used by seagrass and surrounding particulate organic matter. Metagenomic analysis of the epiphytic biofilm on the blades and rhizomes support the ubiquity of microbial ammonification genes in this system. Further, we leveraged the presence of natural carbon dioxide vents and show that microbial ammonification was reduced at lower pH. The presence of <em>P. oceanica</em> enhanced the uptake of nitrogen by water column particulate organic matter, increasing carbon fixation by a factor of 8.6 to 17.4 with the greatest effect at CO<sub>2</sub> vent sites. Seagrass and its microbial associates thus enhanced water column productivity and were a locus for nutrient cycling.</p>
Study of wildfire smoke effects on ecosystem metabolism in 10 California lakes (2018, 2020, 2021)
This dataset was collected as part of a large-scale study to assess impacts of smoke cover on gross primary production (GPP) and ecosystem respiration (R) in California lakes. The 10 study lakes span large gradients in elevation, size, nutrient concentrations, and water clarity. They include 5 ponds and lakes in Sequoia National Park, Lake Tahoe, Dulzura Lake, Clear Lake, Castle Lake, and a site in the Sacramento-San Joaquin River Delta. Metabolic rates in the upper mixed layer of each lake was estimated from hourly in-situ sensor data during the three smokiest years in California since 2006 (2018, 2020, 2021). The dataset includes daily estimates of GPP and R, mean daily values of variables used in metabolism models (water temperature, dissolved oxygen, mixed layer depth, photosynthetically active radiation, wind speed), and mean daily values of metrics related to smoke cover (shortwave radiation, PM2.5, smoke density derived from remote-sensing).
Baltimore Ecosystem Study: Stream metabolism data for core sites in Gwynns Falls
An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the integrated ecosystem functioning of Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the bioreactivity of streams in our watersheds in an attempt to quantify how streams themselves may affect or be affected by water quality. To assess the bioreactivity of streams, we measure whole stream metabolism, which is an integrative metric which quantifies the production and consumption of energy by a stream ecosystem. Stream metabolism represents how energy is created (primary production) and used (respiration) within a stream; it can be thought of as a stream breathing, with primary production being similar to an inhale, and respiration as an exhale. We are monitoring stream metabolism in each of our long-term water quality monitoring stations by deploying sensors that record dissolve oxygen and temperature of the stream every five minutes, and we also have deployed light sensors to record irradiance every five minutes at long-term BES water chemistry streams, which is needed for metabolism modeling. In addition, each dissolved oxygen sensor is located near a USGS gage which estimates discharge every 15 minutes. We used USGS manual discharge estimations linked with
Physical, chemical, and metabolic leaf characteristics within sugar maple in the MELNHE study at Bartlett Experimental Forest, central NH USA, 2017
The MELNHE study looks at patterns of resource limitation through nutrient manipulations in three study sites in New Hampshire: Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook, located in the White Mountain National Forest. The investigation is monitoring stem diameter, leaf area, sap flow, foliar chemistry, leaf litter production and chemistry, foliar nutrient resorption, root biomass and production, mycorrhizal associations, soil respiration, heterotrophic respiration, N and P availability, N mineralization, soil phosphatase activity, soil carbon and nitrogen, nutrient uptake capacity of roots, and mineral weathering. Applications of N and P began in June 2011 and continue at the rate of 30 kg N/ha/yr (as NH4NO3) and 10 kg P/ha/yr (as NaH2PO4). This data set includes physical, chemical, and metabolic leaf characteristics collected in a vertical transect within individual tree crowns. Because trees varied in crown depth, not all trees have the same number of samples collected. Depth from the top of the crown was measured and climbing ropes and a pole pruner were used to collect a population of leaves. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Data for "Combining 13C, 15N, and 2H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study"
<p>This dataset includes raw data used in the paper "Combining <sup>13</sup>C, <sup>15</sup>N, and <sup>2</sup>H tracer to measure feeding and metabolic activity in marine, shallow-water sponges – A pilot study" (JEMBE).</p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <p><strong>Datasets:</strong></p> <ul> <li> <strong>models.zip </strong>- Datasets parameterizing kinetic nonlinear models of a wild-type <em>E. coli </em>strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2: thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3: steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels) (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li><strong>vanilla_learning_training.zip:</strong> contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-<em>{n} </em>( N-10, N-50, N-100, N-500, N-1000, N-72000), where <em>{n} </em>represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li><strong>transfer_learning_training.zip</strong> - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>best_generators.zip</strong> <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>vanilla_ODE_sample_parameters.zip</strong> - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li><strong>ode_solutions_physiology1.zip (available at </strong><a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiology1_ORACLE.zip (available at </strong><a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a><strong>) - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li><strong>ode_solutions_physiologies2-4.zip -</strong> contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li><strong>transfer_learning_ODE_solutions.zip - </strong>contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using <em>{n}</em> samples from physiology 2 and so on (where <em>{n}</em>=10 and 50 respectively). Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul> <p> </p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1_ORACLE.zip</strong> - contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al.</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details.</p> <ul> <li><strong>Temporal evolution of perturbations in non-linear ordinary differential equations</strong> <ul> <li><strong>ode_solutions_physiology1.zip - </strong>contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> </ul> </li> </ul> <p>The detailed instructions and the main body of the dataset is available here: <a href="https://zenodo.org/record/5803120">https://zenodo.org/record/5803120</a></p>
Sleep problems are a strong predictor of stress-related metabolic changes in police officers. A prospective study
<p>Data used for a prospective study on occupational stress, sleep problems and metabolic syndrome in a sample of police officers, Italy, 2009-2014. Paper submitted to PLoS One, waiting for a decision.</p>
A Study to Learn if Recombinant Human Parathyroid Hormone [rhPTH(1-84)] Can Improve Symptoms and Metabolic Control in Adults With Hypoparathyroidism (BALANCE)
ClinicalTrials.gov study NCT03324880. IPD Sharing: YES. Countries: 13. Publications: 1.
Study of Semaglutide for Non-Alcoholic Fatty Liver Disease (NAFLD), a Metabolic Syndrome With Insulin Resistance, Increased Hepatic Lipids, and Increased Cardiovascular Disease Risk (The SLIM LIVER St
ClinicalTrials.gov study NCT04216589. IPD Sharing: YES. Countries: 2. Publications: 2.
Synergistic label-free fluorescence imaging and miRNA studies reveal dynamic human neuron-glial metabolic interactions following injury
Open the record for dataset details and reuse information.
Supplementary material: How should functional relationships be evaluated using phylogenetic comparative methods? A case study using metabolic rate and body temperature
Open the record for dataset details and reuse information.
Stream metabolism study of East Fork Creek, Franklin, TN in years 2022-2023
In East Fork Creek, a headwater stream in middle Tennessee, week-long field campaigns were set up each month from May 2022 to May 2023 to collect stream metabolism estimators. In a more extensive field campaign from July 2-5 in 2022, diel signals were observed for temperature, pH, Ca, Mg, and DIC. Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) were calculated based on a Bayesian model using the dissolved oxygen (DO) time series approach.
Red spruce foliar nutritional and metabolic responses to N, Ca, and N+Ca additions in a plot level study from Hubbard Brook Experimental Forest NH, Adirondack State Park, NY and Groton State Forest, VT from 1997-2000.
Plot Level Ca, N, Ca+N Study Background: Three mature red spruce (Picea rubens Sarg.) – balsam fir (Abies balsamea (L.) Mill.) stands with closed canopies from across the northeastern United States were selected to assess the effects of N and Ca additions on forest growth. These sites included Groton, VT (Groton State Forest), Big Moose Lake, NY (Adirondack State Park) and Watershed 9 in the Hubbard Brook Experimental Forest, NH. Site descriptions can be found in the pdf file included in this dataset. Full site descriptions have been published in Kulmatiski et al, 2007, Nitrogen and calcium additions increase forest growth in northeastern USA spruce–fir forests, Canadian Journal of Forest Research 37: 1574-1585. This publication also provides more in-depth site and soil descriptions. In 1992, three replicate 30 m by 30 m plots were randomly assigned to each of four treatment levels: control (control), Ca addition (Ca), N addition (N), or Ca + N addition (Ca + N) in NY and NH (for a total of 12 plots at each site). In VT, only control and Ca treatments were established due to limited space and research goals (for a total of 6 plots). Beginning in July 1992 through July 2000, N amendments (100 kilograms of NH4NO3-N per hectare per year), Ca amendments (80 kilograms of CaSO4-Ca plus 80 kilograms of CaCl2-Ca per hectare per year), and Ca + N (same dosage as separate applications) amendments were hand broadcast as commercial grade salts. Applications were made in June, July, and August to each plot in each year. SUMMARY: The main objectives of this study were to evaluate changes over time (1997-2000) in foliar metabolism of mature red spruce (Picea rubens Sarg.) trees in response to additions of nitrogen, Ca, or N +Ca supplementation. N was applied as NH4NO3 and Ca was applied as gypsum [CaSO4 .2H2O (2X) + CaCl2 .2H2O (1X)] and Ca + N treatment was a combination of both at the same concentrations Three sites received these treatments; Groton, VT (Groton State Forest), Bi
The zebra mussel (Dreissena polymorpha) as a model organism for ecotoxicological studies: a prior 1H NMR spectrum interpretation of a whole body extract for metabolism monitoring.
<p>NMR data of the zebra mussel <em>Dreissena polymorpha</em> whole body polar extract metabolome</p> <p>- 1D <sup>1</sup>H annotated spectrum - 600 MHz</p> <p>- 2D <sup>1</sup>H-<sup>1</sup>H JRES spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>1</sup>H COSY spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>1</sup>H TOCSY spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 600 MHz</p> <p>- 2D<sup>1</sup>H-<sup>13</sup>C HSQC spectrum - 800 MHz</p> <p>- 2D<sup>1</sup>H-<sup>31</sup>P HSQC spectrum - 800 MHz</p> <p>- <sup>1</sup>H annotated spectrum description tables (.xlsx)</p> <p>- Instructions for data visualization in Topspin</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.