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26,784 results for “M.”
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 273 m^2/kg Blaine, w/c=0.35) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193808">https://doi.org/10.5281/zenodo.7193808</a></p>
Three-dimensional dataset of hydrating cement paste (CEM I Ladce, 391 m^2/kg Blaine, w/c=0.50) in TIFF format
<p>A detailed description of this dataset can be found in <a href="https://doi.org/10.1016/j.dib.2023.108903">https://doi.org/10.1016/j.dib.2023.108903</a><a href="https://doi.org/10.1016/j.dib.2023.108903">.</a></p> <p>This dataset contains a collection of digitized three-dimensional hardened cement paste microstructures obtained from X-ray micro-computed tomography, screened after approx. 1, 2, 3, 4, 7, 14, and 28 days of elapsed hydration at 20˚C in saturated conditions. Each paste specimen had a cylindrical shape (with a diameter of ~1 mm) and was screened at a designated time (as specified in the file name, e.g. “t23hrs”=23 hours of elapsed hydration) and finally saved as an uncompressed and unprocessed *.tif greyscale image data file in 16-bit image depth (as unsigned integers) using a little-endian byte sequence.</p> <p>The dataset contains two sets of images:</p> <p>- “full-sized” digital images stored in a three-dimensional voxel-based matrix with a fixed size of 1100x1100x1100 voxels, denoted as “CEM_I_Ladce_*” in the file name; each file size amounts to ~2.5 GB and contains the whole screened specimen with a variable voxel size in the range 1.0913 − 1.1174 µm depending on the particular specimen (as specified in the file name, e.g. “1d1174um”=1.1174 µm/voxel)</p> <p>- smaller image subvolumes, denoted as Region Of Interest (ROI), extracted from the interior of the full-sized specimen from an arbitrary location, and denoted as “filteredROI_*” in the file name; this cropped ROI has a cubic shape and stores a three-dimensional voxel-based matrix with a fixed size of 500x500x500 µm<sup>3</sup> constituted by a variable voxel count (given the fluctuating voxel size for each specimen, see above). Both the exact voxel count (i.e. three-dimensional matrix dimensions) and voxel size are further specified in each file name. A sequence of imaging filters was sequentially applied to this ROI to further enhance the contrast among the different microstructural phases, see <a href="https://doi.org/10.1016/j.cemconcomp.2022.104798">10.1016/j.cemconcomp.2022.104798</a> for details.</p> <p>Note that the same dataset stored in *raw format is available from <a href="https://doi.org/10.5281/zenodo.7193798">https://doi.org/10.5281/zenodo.7193798</a></p>
Simulated data set for SAXS tensor tomography, sample "M"
<p>This is a simulated data set intended for testing SAXS tensor tomographic reconstructions, including the original field from which the data was generated.</p>
Ofertas de H&M en Europa: tipo de oferta, nombre del producto y precio.
<p>El dataset contiene información sobre todos los productos de H&M que se encuentran en la sección de ofertas en todas las páginas web de la marca en países de Europa.</p> <p>El dataset consta de los siguientes campos: </p> <p><strong>main page</strong> - la dirección URL de la oferta donde puedes encontrar el producto (cadena de caracteres).</p> <p><strong>name</strong> - el nombre del producto (cadena de caracteres).</p> <p><strong>url</strong> - la dirección URL del producto (cadena de caracteres).</p> <p><strong>price</strong> - el precio del producto en moneda local (cadena de caracteres).</p> <p>El dataset contiene 42910 registros, extraidos de las páginas de H&M en Europa</p> <p> </p>
Dataset associated to Janas, D. M. et al., Adv. Mater. 2022, 2205698
<p>The raw data that support the findings of the study "Enhancing Electron Correlation at a 3d Ferromagnetic Surface" (https://doi.org/10.1002/adma.202205698). The data analysis was performed using the software "ImageJ" and "Igor Pro 8". Experimental files (.pxp) of the processed data are available from the corresponding authors upon reasonable request.</p>
Figures ĮĮ–Į3. Marimatha male genitalia. ĮĮ M. nigrofimbria Į2 M. squala Į3 M. quadrata. in Review of the North American species of Marimatha Walker with descriptions of three new species (Lepidoptera, Noctuidae, Eustrotiinae) and the description of Pseudomarimatha flava (Noctuinae, Elaphriini), a new genus and species confused with Marimatha
Figures ĮĮ–Į3. Marimatha male genitalia. ĮĮ M. nigrofimbria Į2 M. squala Į3 M. quadrata.
ChinaWheatYield30m: A 30-m annual winter wheat yield dataset from 2016 to 2021 in China
<p>Generating spatial crop yield information is of great significance for academic research and guiding agricultural policy. Most existing public yield datasets have a coarse spatial resolution. Although these datasets are useful for analyzing regional temporal and spatial change, they cannot deal with spatial heterogeneity, which happens to be the most significant characteristic of the Chinese small-scale farmers' economy. Hence, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016-2021. </p>
Dataset for publication Sikora P., Woliński P., Chougan M., Madraszewski S., Węgrzyński W., Papis B.K., Federowicz K., Ghaffar S.H., Stephan D. A systematic experimental study on biochar-cementitious composites: Towards carbon sequestration. Industrial Crops and Products (2022) 184, 115103
<p>Open Dataset of: TG, XRD data and Origin file (*.opju) of particle size distribution, calorimetry, compressive strength (mortar + paste), water absorption coefficient/open porosity from publication Sikora P., Woliński P., Chougan M., Madraszewski S., Węgrzyński W., Papis B.K., Federowicz K., Ghaffar S.H., Stephan D. A systematic experimental study on biochar-cementitious composites: Towards carbon sequestration. Industrial Crops and Products (2022) 184, 115103. https://doi.org/10.1016/j.indcrop.2022.115103<br> 122758</p>
Data for m-NLP inference models using simulation and regression techniques
<p>This file contains data for the manuscript ``m-NLP inference models using simulation and regression techniques''.</p> <p>The "needle probe data.xlsx" file contains the simulation results and the fits to the simulation data. It also contains the coefficients a, b, and c for which a synthetic solution library can be constructed. Examples of the synthetic solution library are also included, named "nndlt.dat" and "nndlv.dat". The "center.out" file contains the Radial Basis Function density inference model created from the synthetic solution library.</p> <p>The NorSat-1 data can be obtained from: http://tid.uio.no/plasma/norsat/norsat1.html</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Data for: Trinity assembled transcriptome of a Eurasian (Myriophyllum spicatum) and a hybrid (M. spicatum × M. sibiricum) genotype of watermilfoil
<p>Aquatic plant managers frequently treat Eurasian watermilfoil (<em>Myriophyllum spicatum</em> L.; EWM) and hybrid watermilfoil (<em>Myriophyllum spicatum</em> L. × <em>Myriophyllum sibiricum</em> Komarov) with 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide. However, watermilfoil genotypes can differ in their response to 2,4-D. In this study, we compared facultative and constitutive gene expression differences for two watermilfoil genotypes (one Eurasian and one hybrid) that differ in their sensitivity to 2,4-D. To do this, we compared between control and 0.5mg L-1 2,4-D treated plants at four time points after treatment. We also assembled the first de novo watermilfoil transcriptome. We found that while qualitatively similar, the facultative transcriptional response of the EWM genotype to 2,4-D treatment was much stronger than the hybrid genotype, indicated by a greater number and log-fold-change of differentially expressed genes at all time points after treatment. Further, we found that the EWM and hybrid genotype differed in their 9-cis-epoxycarotenoid dioxygenase (NCED) and abscisic acid (ABA) gene response, and that there was a greater amount of photosynthesis gene downregulation (both in number and log-fold-change) in the EWM than the hybrid genotype. At the constitutive level, overall, the hybrid expressed genes at a higher level than the EWM genotype, but not the genes of the 2,4-D response pathway. These differences in gene expression match with the degree of phenotypic difference in growth observed between these genotypes when exposed to 2,4-D. The hybrid genotype used here mitigates the effects of 2,4-D treatment better than the EWM genotype at both the molecular and phenotypic level. More study is needed to understand the mechanism(s) of mitigation and whether this is a cause of hybridity, or the specific genotypic backgrounds used here.</p>
Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness
<p>Kremer-Grest model polymer melts of highly entangled linear chains. Each melt has approximately 1000 chains of Z=200 entanglements each. Systems have been generated for integer and half-integer stiffness kappa=-2.0 to 6.0. System sizes range from 25M to 2M beads.</p> <p>For details regarding the equilibration process and the Kremer-Grest polymer model see C. Svaneborg & R. Everaers ""Multiscale equilibration of highly entangled isotropic model polymer melts" J. Chem. Phys. 158, 054903 (2023) <a href="https://doi.org/10.1063/5.0123431">https://doi.org/10.1063/5.0123431</a></p> <p>Filenames denote the kappa<value> used when equilibrating the melt as well as the number of entanglements Z<number> and the number of molecules M<number>. The files are in ASCII format in the format of a LAMMPS data file. (https://lammps.sandia.gov) The semantics is self-explanatory, sections contains id, molecule, unwrapped coordinates of all beads, as well as bond and angular interactions between all beads.</p> <p>We acknowledge that part of the results of this research was obtained using the PRACE Research Infrastructure resource Joliot-Curie SKL based in France at GENCI@CEA. Computing facilities were provided by the eScience Center at the University of Southern Denmark and financed by the Faculty of Science.</p> <p>Please cite as:</p> <p>@article{MultiscaleEquilibrationHighlyEntangledIsotropicModelPolymerMelts,<br> author = {Svaneborg,Carsten and Everaers,Ralf },<br> title = {Multiscale equilibration of highly entangled isotropic model polymer melts},<br> journal = {J. Chem. Phys.},<br> volume = {158},<br> number = {5},<br> pages = {054903},<br> year = {2023},<br> doi = {10.1063/5.0123431},</p> <p> URL = {https://doi.org/10.1063/5.0123431}</p> <p>}</p> <pre>@misc{EquilibratedKGMeltsZ200, author = {Svaneborg,Carsten and Everaers,Ralf}, title = {Equilibrated Kremer-Grest polymer melts of M=1000 linear chains with Z=200 entanglements for varying chain stiffness.}, month = feb, year = 2023, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.7034881}, url = {https://doi.org/10.5281/zenodo.7034881} }</pre>
Fig. 4. M in A new hangingfly (Insecta: Mecoptera: Bittacidae) from the Middle Jurassic of Inner Mongolia, China
Fig. 4. M. daohugouensis holotype, camera lucida drawing of right fore wing. Scale bar = 2 mm.
Fig. 2. M in A new hangingfly (Insecta: Mecoptera: Bittacidae) from the Middle Jurassic of Inner Mongolia, China
Fig. 2. M. daohugouensis holotype, camera lucida drawing. Scale bar = 2 mm.
Code and Output data associated with M.E. Orive, M, Barfield, R.D. Holt. Partial clonality expands the opportunity for spatial adaptation. Submitted to The American Naturalist.
<p>This contains the source code, all output files and sample input files used to create the graphs in the manuscript " M.E. Orive, M, Barfield, R.D. Holt. Partial clonality expands the opportunity for spatial adaptation," submitted to The American Naturalist. The readme.rtf file summarizes the manuscript, lists all code files and output files, and explains the output file format and which output data was used, organized by figure number.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L2)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L1)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L3)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000.</p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L4)
<p>Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. </p> <p> </p> <p>File naming convention:</p> <p>2001..2020 = time reference: period 2001-2020,</p> <p>QTP_DNN_Sm = Dataset ID,</p> <p>L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm),</p> <p>day1..day365/day366 = Date order within the year (January 1st - December 31st),</p> <p>pkl = Data storage format.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.