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65 results for “Ice melting”
Ice-melt and annual CO2-evasion of arctic lakes
<p>Dataset to the article: "<i>Ice-melt period dominates annual carbon dioxide evasion from clear-water Arctic lakes</i>".</p><p>The data concern the physicochemical parameters and CO2 evasion for the 14 studied lakes (sheet 1) Furthermore, ice-melt CO2 evasion, annual CO2 evasion and the ratio ice-melt:annual evasion, and DOC for the 14 studied and additional lakes from earlier publications is given (sheet 2).</p>
Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>This dataset contains the data and scripts for the publication "<a href="https://doi.org/10.1029/2023MS003829">Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks</a>" in <em>Journal of Advances in Modeling Earth Systems</em>.</p> <p>Before going into details, here is a reminder that the NEMO runs for the training dataset are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. For the testing dataset: 'bf663' is the REPEAT1970 run and 'bi646' is the 4xCO2 run.</p> <p>Most of the formatting and preprocessing of the training data has been made for <a href="https://tc.copernicus.org/articles/16/4931/2022/">Burgard et al. 2022</a>. The raw and to-some-degree processed data can therefore be found here: <a href="https://doi.org/10.5281/zenodo.7308352">https://doi.org/10.5281/zenodo.7308352</a>. <br>The raw data for the testing dataset is from <a href="https://doi.org/10.1029/2021MS002520">Smith et al. 2021</a>, you can find it here: <a href="doi.org/10.5281/zenodo.7886986">https://doi.org/10.5281/zenodo.7886986</a></p> <p>The following folders and files can be found here:</p> <p>===============<br><strong>raw/</strong></p> <p>Some geometrical files needed for initial data formatting and masking.</p> <p>===============<br><strong>interim/</strong></p> <ul> <li><strong>ANTARCTICA_IS_MASKS</strong>/ (<em>from INTERIM_ANTARCTICA_IS_MASKS.zip</em>): contains <ul> <li>masks and geometric information for the testing dataset to be included in the input file of the neural network and for the classic parameterisations.</li> <li>local bedrock and ice meridional and zonal slopes</li> </ul> </li> <li><strong>BOXES/</strong> (<em>from INTERIM_BOXES.zip</em>): contains variables needed to apply the box parameterisation for the testing dataset</li> <li><strong>PLUMES/ </strong>(<em>from INTERIM_PLUMES.zip</em>): contains the variables needed to apply the plume parameterisation for the testing dataset</li> <li><strong>SMITH_bf663/</strong><em><strong> and </strong></em><strong>SMITH_bi646/ </strong>(<em>from INTERIM_SMITH*.zip</em>): for testing dataset, <ul> <li>corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land</li> <li>custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land</li> <li>isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid</li> <li>other_mask_vars_Ant_stereo.nc: contains other variables used for the masks</li> <li>the reference melt: 1D containing integrated melt, 2D containing melt fields, box1 containing melt near the grounding line</li> </ul> </li> <li><strong>T_S_PROF/ </strong>(<em>from INTERIM_T_S_PROF.zip</em>) <ul> <li>Mean profiles used as input for traditional parameterisations</li> <li>T and S 2D fields, extrapolated from the mean profiles to the local ice draft depth (needed as input for the neural network)</li> <li>Fields of mean and standard deviation T and S for all points (needed as input for the neural network)</li> </ul> </li> <li><strong>NN_MODELS/</strong><em><strong> </strong>(from </em>INTERIM_NN_MODELS<em>.zip</em>) contains all neural networks trained for this paper (for the cross validation and over the whole dataset for testing)</li> <li><strong>INPUT_DATA/ </strong>(from INTERIM<em>_</em>INPUT_DATA.zip) contains all input csv files containing the input datasets for the different training and testing iterations. Also contains the metrics to normalise the input. For the cross-validation, the input csv files are not included because they are too large. However, they can be reconstructed from the individual files for ice shelves and time blocks. The metrics to normalise the data during the cross-validation are included in EXTRAPOLATED_ISFDRAFT_CHUNKS_CV!</li> </ul> <p>===============<br><strong>processed/MELT_RATE/</strong></p> <p>Contains resulting melt rates</p> <ul> <li><strong>CV_ISF :</strong> Cross-validation results over ice shelves</li> <li><strong>CV_TBLOCKS : </strong>Cross-validation results over time</li> <li><strong>SMITH_bf663 : </strong>Neural network results for REPEAT1970</li> <li><strong>SMITH_bf663_CLASSIC : </strong>"Traditional" parameterisation results for REPEAT1970</li> <li><strong>SMITH_bi646 :</strong> Neural network results for 4xCO2</li> <li><strong>SMITH_bi646_CLASSIC:</strong> "Traditional" parameterisation results for 4xCO2</li> </ul> <p>=====================</p> <p>The explanation around the scripts can be found in README.rst with the scripts in<strong> scripts_paper_simpleNN_basal_melt.zip</strong>.<br><em>Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: </em><a href="https://github.com/ClimateClara/https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt"><em>https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt</em></a>, <em>find the most up-to-date version of the package 'multimelt' here: </em><a href="https://github.com/ClimateClara/multimelt"><em>https://github.com/ClimateClara/multimelt</em></a><em> and a version you can install via pip here: </em><a href="https://github.com/ClimateClara/multimelt"><em>https://pypi.org/project/multimelt/</em></a></p> <p>Finally, if anything is unclear, check out the "Methods" section of the paper: <a href="https://doi.org/10.1029/2023MS003829">https://doi.org/10.1029/2023MS003829</a></p>
Seafloor roughness reduces melting of the East Antarctic ice sheets
<p>MITgcm model setup and<span> </span>MATLAB script and data that support figures for "<strong>Seafloor roughness reduces melting of East Antarctic ice shelves</strong>" by Y. Liu, M. Nikurashin, and B. Pena-Molino</p> <p><strong>MITgcm model setup: </strong></p> <p>We provide the two MITgcm model configurations of the Denman regional model, using BedMachine and SRTM15+ bathymetry datasets, described in the main text of the paper. Both simulations can be run from a pickup file corresponding to 5 years from the beginning of the simulation, when the model is well equilibrated. Complete model outputs used for the analysis in the paper can be produced by running the simulations for additional 5 years.</p> <p>(Contents)</p> <ul> <li><strong><em>denman_0025_RYF_SHI_tides_bedmachine.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with BedMachine bathymetry)</li> <li><strong><em>denman_0025_RYF_SHI_tides_srtm15.zip</em></strong> (code, parameter files, and initial and boundary conditions to run the simulation with SRTM15+ bathymetry)</li> <li><strong><em>denman_external_forcing_files.zip</em></strong> (3-hourly atmospheric forcing files that are used for both simulations take nearly 100Gb of disk space. Due to Zenodo size limit of 50GB, we provide the original JRA-55 forcing files and a Matlab script that interpolates them onto the regional model grid.)</li> </ul> <p>(How to build and run)</p> <p>The reader is referred to MITgcm documentation for instructions on how to download, compile and run the model: https://mitgcm.readthedocs.io/en/latest/getting_started/getting_started.html</p> <p><strong>MATLAB script and data:</strong></p> <ul> <li><strong><em>data & script.zip</em></strong> includes the raw data saved in Matlab data format and the script to create the figures in the paper. Please download all files into a folder and run the script.m under MATLAB.</li> </ul>
Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf
<p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(1) MLP_model_surface_melt_corr.h5</strong> is the developed MLP model used for correcting RACMO2 surface melt.</p> <p><strong>(2) RACMO2_surface_melt_corr_MLP_AWS14.xlsx </strong>corrected surface melt [mm w.e. per day] from RACMO2 at AWS 14 during austral summers 2001 - 2016. The model inputs are (1) the simulated albedo, (2) the albedo difference between the observed and simulated albedo, (3) air temperature at 2m, (4) incoming shortwave radiation, (5) downwelling longwave radiation, (6) simulated surface melt, (7) Boolean melt flag, (8) surface melt difference to the previous day, and (9) record date as day of the year.</p> <p><strong>(3) RACMO2_surface_melt_corr_MLP_AWS17.xlsx </strong>The same as point 2 but for AWS 17</p> <p><strong>(4) RACMO2_surface_melt_corr_MLP_AWS18.xlsx </strong>The same as point 2 but for AWS 18</p> <p>Note: Data 2-4 are corrected RACMO2 simulations of surface melt at the pixels in RACMO2 27 km grid corresponding to AWS 14, 17, and 18 locations. They are not AWS observations.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Related data set:</strong></p> <p>MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid product is available via the Land Processes Distributed Active Archive Center (LP DAAC) (https://doi.org/10.5067/MODIS/MOD09GA.006, last access: 3 December 2021). MODIS/Terra+Aqua Albedo Daily L3 Global 500 m SIN Grid product is also available via LP DAAC (https://doi.org/10.5067/MODIS/MCD43A3.006, last access: 3 December 2021). Sentinel-1 images are provided by the European Space Agency (ESA) (https://sentinel.esa.int/web/sentinel/sentinel-data-access, last access: 3 December 2021). Automatic weather station observations from AWS 14, 17, and 18 are available via https://doi.pangaea.de/10.1594/PANGAEA.910473 (last access: 3 December 2021). RACMO2 simulations (https://www.projects.science.uu.nl/iceclimate/models/antarctica.php#2-1, last access: 3 December 2021) are provided by van Wessem et al. (2018) which are available on request to the original authors.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>You should also refer to and cite the following paper:</strong></p> <p>Hu, Z., Kuipers Munneke, P., Lhermitte, S., Izeboud, M., and van den Broeke, M.: Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-102, in review, 2021.</p>
Data for manuscript 'The response of Ross Sea shelf water properties to enhanced Amundsen Sea ice shelf melting'
<p>Data for manuscript 'The response of Ross Sea shelf water properties to enhanced Amundsen Sea ice shelf melting'.</p> <p>This repository contains the average data for the last 5 years for each sensitivity experiment used for analysis and processed data used to generate the figures in the manuscript.</p>
Supplementary data for the publication: 'Impact of ice topography, basal channels and subglacial discharge on basal melting under the floating ice tongue of 79N Glacier, northeast Greenland'
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First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
A numerical study on melt water feedback in the coupled Arctic Sea ice-ocean system
<p>This dataset contains one-dimensional model code and input files for studying the effects of melt water on upper ocean stratification and sea ice melt and growth. The model used in this study is the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m.</p> <p>code_1frw: contains control run configuration. </p> <p>code_08frw: contains sensitivity experiment (MWP-80% run) configuration. </p> <p>code_06frw: contains sensitivity experiment (MWP-60% run) configuration. </p> <p>code_04frw: contains sensitivity experiment (MWP-40% run) configuration. </p> <p>code_02frw: contains sensitivity experiment (MWP-20% run) configuration. </p> <p>code_0frw: contains sensitivity experiment (MWP-0% run) configuration. </p> <p>input: contains parameter settings and input files for all experiment.</p> <p>model results: the model results used in this paper.</p>
Data supporting 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'
<p>This data set contains the data produced for the paper 'Modeling Antarctic ice shelf basal melt patterns using the one-Layer Antarctic model for Dynamical Downscaling of Ice--ocean Exchanges (LADDIE v1.0)'</p> <p>The data set contains output from LADDIE simulations, including basal melt rates.</p> <p>The main simulations used in the man text are:</p> <p>- Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc">CrossDots_0.5_tanh_Tdeep0.4_ztcl-500_050.nc </a><br> - Filchner-Ronne: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/FRIS_1.0_linear_S134.8_T1-2.3_720.nc">FRIS_1.0_linear_S134.8_T1-2.3_720.nc </a><br> </p> <p>The additional simulations included in the Appendix are:</p> <p>- 3D forcing of Crosson-Dotson: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/CrossDots_0.5_mitgcm_2003_2008_100.nc">CrossDots_0.5_mitgcm_2003_2008_100.nc </a><br> - Tuning of Crosson-Dotson: XX_YY_ZZ.nc, where XX is the resolution in km, YY is the value for Cd,top, and ZZ is the value for Dmin<br> - Pine Island Ice Shelf: <a href="https://zenodo.org/api/files/0640a922-97a9-4ced-ad3d-dab66f05c696/PIG.nc">PIG.nc </a><br> </p>
Supplementary video of Petrini et al. 2023, submitted to TC. "Topographically-controlled tipping point for complete Greenland Ice Sheet melt"
<p>Animations showing the evolution of the Greenland Ice Sheet in simulations with different SMB and global mean temperature levels. </p>
Data from: Ground ice melt in the high Arctic leads to greater ecological heterogeneity
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Arctic sea ice snow melt onset dates from the Advanced Horizontal Range Algorithm, version 5 (1979 - 2022)
<h2>Data</h2><p>This data set includes one NetCDF (.nc) file containing the full set of annual Arctic sea ice melt onset dates and statistical summaries for the 1979 - 2022 period derived with the Advanced Horizontal Range Algorithm (AHRA) V5. The remaining .png files include browse images of each data layer contained within the primary NetCDF file.</p><p>A full description of the data provided herein can be found in the following publication: </p><p>Bliss, A. C. (submitted 2023), Passive microwave observations of Arctic sea ice melt onset from the Advanced Horizontal Range Algorithm 1979 – 2022, <i>Scientific Data</i>.</p><h2>Future updates</h2><p>This data set is distributed on an ongoing basis by the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. Future updates to the AHRA V5 data set including annual updates of the data product will be available at the NSIDC archive below:</p><p>Bliss, A. C., M. Anderson, and S. Drobot. (2022). Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. <a href="https://doi.org/10.5067/TRGWQ0ONTQG5">https://doi.org/10.5067/TRGWQ0ONTQG5</a>.</p>
Assets for "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers"
<p>Experimental data reported in "Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice shelf from Isochronal Layers".</p><p>Contains processed simulation data for both synthetic and Ekström Ice Shelf experiments. Selected best layers for each Internal Radar Horizons in the respective simulated datasets are contained in "all_layers_final.p", and the respective mass balance parameters for those simulations in "all_mbs_final.p".</p><p>Trained Neural Posterior Estimator (NPE) models for each IRH are found in "inference.p" files, and posterior predictive simulations in "post_predictive.p" files for each posterior respectively. Code to visualise and plot this data is found in https://github.com/mackelab/sbi-ice.</p>
Diverse impacts of sea ice and ice shelf melting on phytoplankton communities in the Cosmonaut Sea, East Antarctica
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Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 5
This data set includes yearly snow melt onset dates over Arctic sea ice derived from Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperature measurements. The data are gridded to the 25 km Northern Hemisphere Polar Stereographic projection and available from 1979 through 2022. One browse image is available for each year. This data set also contains value-added statistics for each grid cell, including: mean melt onset date, latest (maximum) melt onset date, earliest (minimum) melt onset date, range of melt onset dates (the difference between maximum and minimum onset dates), and the standard deviation of melt onset dates. One browse image is also provided for each statistical field.
Greenland Ice Sheet Melt Characteristics Derived from Passive Microwave Data, Version 1
The Greenland ice sheet melt extent data, acquired as part of the NASA Program for Arctic Regional Climate Assessment (PARCA), is a daily (or every other day, prior to August 1987) estimate of the spatial extent of wet snow on the Greenland ice sheet since 1979. It is derived from passive microwave satellite brightness temperature characteristics using the Cross-Polarized Gradient Ratio (XPGR) of Abdalati and Steffen (1997). It is physically based on the changes in microwave emission characteristics observable in data from the Scanning Multi-channel Microwave Radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) instruments when surface snow melts. It is not a direct measure of the snow wetness but rather is a binary indicator of the state of melt of each SMMR and SSM/I pixel on the ice sheet for each day of observation. It is, however, a useful proxy for the amount of melt that occurs on the Greenland ice sheet. The data are provided in a variety of formats including raw data in ASCII format, gridded daily data in binary format, and annual and complete time series climatologies in gridded binary and GeoTIFF format. All data are in a 60 x 109 pixel subset of the standard Northern Hemisphere polar stereographic grid with a 25 km resolution and are available via FTP.
Dynamic regimes of the Greenland Ice Sheet emerging from interacting melt-elevation and glacial isostatic adjustment feedbacks - Dataset
<p>Research data for the publication "Dynamic regimes of the Greenland Ice Sheet emerging from interacting melt-elevation and glacial isostatic adjustment feedbacks" by Maria Zeitz, Jan M. Haacker, Jonathan F. Donges, Torsten Albrecht, and Ricarda Winkelmann, accepted for Earth System Dynamics in 2022.</p> <p> </p> <p>Find the output of the simulations with the Parallel Ice Sheet Model PISM in the pism_out* archives, the binary, the run scripts, the input data and the analysis scripts in the "other" archive.</p> <p> </p> <p> </p>
MITgcm model setup and output for "Submesoscale variability and basal melting in ice shelf cavities of the Amundsen Sea"
<p>Here, it contains the results of the high-res eastern AMS simulation. A detailed description of the model configuration and model output is provided by Nakayama et al.,2019.</p><p>Nakayama, Yoshihiro, Georgy Manucharyan, Hong Zhang, Pierre Dutrieux, Hector S. Torres, Patrice Klein, Helene Seroussi, Michael Schodlok, Eric Rignot, and Dimitris Menemenlis. "Pathways of ocean heat towards Pine Island and Thwaites grounding lines." <i>Scientific reports</i> 9, no. 1 (2019): 16649.</p><p>Due to space limitations, please access NASA data for all other model daily outputs (Registration is required). https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/AMS_200m. </p><p>Contents can be downloaded easily using wget (see link below).<br>https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p><p>(Contents)<br>code.zip (code to run this simulation)<br>input_ctrl.zip (input file required for this simulation <br>input_nomelt.zip (input file required for this simulation <br>results_ctrl.zip (days 30 and 60))<br>results_nomelt.zip (days 30 and 60))<br>Caution: You need to download heavier input files to rerun this simulation from ECCO-Drive. <br><br>(How to build and run)<br>mkdir build<br>./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br>make depend<br>make -j 16<br>cd ..<br>mkdir test<br>cd test<br>ln -sf ../input/* .<br>ln -sf /nobackup/hzhang1/forcing/era_xx_it33/ .<br>cp ../build/mitgcm_uv .<br>qsub run8_sandy_tracer_init_cont_2.pbs</p>
Sea Ice Melt Pond Data from the Canadian Arctic, Version 1
This data set contains observations of albedo, depth, and physical characteristics of melt ponds on sea ice, taken during the summer of 1994. The melt ponds studied were located just south of Cornwallis Island in the Barrow Strait, Nunavat, Canada. The reflectance spectra were acquired with a portable spectrometer. Cloud conditions and ice type were recorded as well.
Contribution to High Asia Runoff from Ice and Snow (CHARIS) Melt Model Output, 2001 - 2014, Version 1
This data set contains input and output data for temperature index (TI) model runs completed for the Contributions to High Asia Runoff from Ice and Snow (CHARIS) project at NSIDC in 2018 and 2019. The input data are the area of snow on land, snow on ice, and exposed glacier ice as well as surface air temperature. These inputs are used to model the volumes of melt runoff from the snow on land, snow on ice, and exposed glacier ice in certain areas of High Mountain Asia.
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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.