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676 results for “hydrologic”
Fig. 5. a in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 5. a) Analysis of yield per recruitment for different size at first capture. b)Spawning potential ratio (SPR) over time based on the estimated exploitation rate (μ) and the capture length. PS76: Puerto Sánchez 1976; PS02: Puerto Sánchez 2002; V89: Victoria 1989; V94: Victoria 94; V05: Victoria 2005; H05: Helvecia 2005.
Fig. 4 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 4. Variation in the mean length (wide bars), the maximum length (thin bars), and the mesh size (points) between 1976 and 2005.
Fig. 2 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 2. Capture records of sábalo in the Paraná basin. The data taken from 1920 to 1988 were compiled by the National Agency of Freshwater Fisheries. The data from 1994 to 2009 correspond to the exportation records of the Sanitary National Service (SENASA). No data from 1988 to 1993 were available.
Fig. 1 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 1. Study area and the location of the main landing sites of the sábalo fisheries in the lower Paraná basin.
Fig. 3. a in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 3. a) Variation of average flow from 1920 to 2009. b) Variation in the channel-connectivity index (CCI) from 1920 to 2009, expressed as a percent of the time that the water level surpasses the bankfull level (3 m). c) Variation of the floodplain connectivity index (FCI) from 1920 to 2009, expressed as the percentage of the time that the water level surpassed a height of 4.3 m flooding the alluvial valley. Vertical grey lines indicate different hydrological periods considered in this study.
Fig. 4 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 4. NMDS ordination of dominant fish for different hydrologic condition (HW = high water, FW receding water, IS isolation) and sites (S). Ope = Odontostilbe pequira, Cvo = Cyphocharax voga, Spi = Serrapinnus calliurus, Abi = Astyanax bimaculatus, Pli = Prochilodus lineatus, Mdi = Moenkhausia dichroura, Gba = Gymnogeophagus balzanii, Rbo = Roeboides microlepis, Opa = Odontostilbe paraguayensis, Dte = Diapoma terofali.
Fig. 2 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 2. Water level fluctuations of the Paraná River at Corrientes between 1997 and 2001. The Sites were connected with the Paraná River above the hydrological level indicated by the horizontal lines. The number of flooding days (in parentheses) indicates the connectivity between the floodplain and the river channel.
Fig. 3 in The role of vegetated areas on fish assemblage of the Paraná River floodplain: effects of different hydrological conditions
Fig. 3. Cluster analysis based on Jaccard distance (UPGMA method) of fish assemblages in the seven floodplain lakes. A. March 1999 (after a long lasting inundation phase of the Paraná River), B-September 1999 (at receding water) and C- February 2000 (during isolation).
Hydrologic and Isotopic Data from the Montepaldi Experimental Vineyard, Tuscany, Italy
<div> <div>This dataset includes soil moisture and water stable isotope data collected at the Montepaldi farm (Tuscany, Italy) in the summer 2021. The data is discussed in a paper which was published in 2024 on the journal Ecohydrology:</div> <div> </div> <div>Benettin, P., Tagliavini, M., Andreotti, C., Manca di Villahermosa, F., Verdone, M., Dani, A. and Penna, D. (2024), Ecohydrological Dynamics and Temporal Water Origin in a European Mediterranean Vineyard. Ecohydrology e2711. <a href="https://doi.org/10.1002/eco.2711">https://doi.org/10.1002/eco.2711</a></div> <div> </div> </div>
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 1. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 1). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 1. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 1). Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Cycles in hydrologic intensification and de-intensification create instabilities in spring nitrate-N export C-Q behavior in northern temperate forests
<p>Data supporting analysis in "Cycles in hydrologic intensification and de-intensification create instabilities in spring nitrate-N export C-Q behavior in northern temperate forests".</p> <p>The worksheet "daily SWE" contains modeled daily snow water equivalent (SWE; mm) at Turkey Lakes Watershed. Daily values were modeled using a snow accumulation and melt routine within a hydrologic model that used measured precipitation and snow survey data as input and was calibrated to daily discharge measurements at a reference catchment. </p> <p>For use of SWE data, please cite:<br>Leach, J. A., Buttle, J. M., Webster, K. L., Hazlett, P. W., & Jeffries, D. S. (2020). Travel times for snowmelt-dominated headwater catchments: influences of wetlands and forest harvesting, and linkages to stream water quality. Hydrological Processes, 34(10), 2154-2175. https://doi.org/10.1002/hyp.13746</p> <p>The worksheet "monthly T, P, PET" contains monthly mean temperature (°C), monthly total precipitation (mm), and monthly Hamon potential evapotranspiration (PET; mm) from measurements made at the Algoma CAPMoN (Canadian Air and Precipitation Monitoring Network) meteorological station (47°02′N, 84°22′W, 411 m.a.s.l.).</p> <p>For use of these climate data, please cite:<br>Semkin, R. G., Jeffries, D. S., Neureuther, R., Lahaie, G., McAulay, M., Norouzian, F., & Franklyn, J. (2012). Summary of hydrological and meteorological measurements in the Turkey Lakes Watershed, Algoma, Ontario, 1980-2010. Water Science and Technology Directorate Contribution No. 11-145. Environment Canada, National Water Research Institute, Burlington, ON, 85 p.</p>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
Data from: Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios
<p>Datasets for manuscript "Andres, K. J., Chien, H., and Knouft, J. H. Hydrology induces intraspecific variation in freshwater fish morphology under contemporary and future climate scenarios. Science of the Total Environment. <a href="https://doi.org/10.1016/j.scitotenv.2019.03.292">https://doi.org/10.1016/j.scitotenv.2019.03.292</a>"</p> <p>landmarks.zip: landmarks digitized on images of 1081 specimens using TpsDig2 software.</p> <p>streamflow_estimates.csv: Contemporary (1980-2009) and future (2070-2099) streamflow estimates [avg: average annual streamflow discharge (m3 s-1); cv: coefficient of variation of annual discharge] in sub-basins containing populations of 6 minnow species in IL, USA</p>
A Long-Term Micrometeorological and Hydrological Dataset Across an Elevation Gradient in Sagehen Creek, Sierra Nevada, California
<p>We compile and release ~55 years of daily and ~20 years of hourly Micrometeorological and hydrological data from Sagehen Creek a 28 km〖^2〗 watershed with observation sites spanning 1771 to 2670 m. A USGS gauging station measures streamflow at the catchment outlet. There are three Snow Telemetry (SNOTEL) stations: Independence Camp(2128 m), Independence Creek (1962 m) and Independence Lake (2541 m) that measure hourly precipitation, temperature, soil moisture (at 5, 20, and 50 cm), as well as daily snow water equivalent (SWE) and snow depth. A new method was used to estimate hourly precipitation data using quality controlled daily totals. A NOAA cooperative observer (COOP) station measures daily precipitation, temperature, SWE, and snow depth from 1953-1997 and then measures hourly precipitation, temperature, SWE, and snow depth, relative humidity, and solar radiation from 2001 through 2017 2001-present. There are an additional three towers with data beginning in 2009 measuring snow depth, SWE, solar radiation, barometric pressure, precipitation, relative humidity, and temperature: Tower 1 (1934m), Tower 3 (2114 m), and Tower 4 (2350 m). Wind speed, temperature, and relative humidity measured at 7.6 and 30.5 m at each site. Data from all stations were checked for poor QA/QC and substantial and sophisticated gap-filling techniques were deployed. This dataset holds potential for improving understanding of orographic processes and their implications for streamflow generation in a groundwater-dominated watershed.</p>
The Effect of QPF on Real-time Deterministic Hydrologic Forecast Uncertainty
<p>The use of Quantitative Precipitation Forecast (QPF) in hydrologic forecasting is commonplace, but QPF is subject to considerable error. When QPF is included as a model forcing in the hydrological forecast process, significant error is passed to subsequent hydrologic predictions. Two questions arise: (1) are the resulting observed hydrologic forecast errors sufficiently large to suggest the use of zero QPF in the forecast process; if the use of QPF is indicated, (2) how many periods (hours) of QPF (1-, 6-, 12-,..., 72-h...) should be used? Also, do forecast conditions exist under which the use of QPF should be different? This study presents results from two real-time hydrologic forecast experiments, focused on the NOAA/NWS Ohio River Forecast Center (OHRFC). The experiments rely on forecasts from subbasins at 38 forecast point locations, ranging in drainage area, geographic location within the Ohio River Valley, and watershed response time. Results from an experiment, spanning all flow ranges, for the August 10, 2007 - August 31, 2009 period, show that non-zero QPF produces smaller hydrologic forecast error than zero QPF. A second experiment, January 23, 2009 through September 15, 2010, suggests that QPF should be limited to 6- to 12-h duration for flood forecasts. Beyond 12-h, hydrologic forecast error increases substantially across all forecast ranges, but errors are much larger for flood forecasts. Increased durations of QPF produce smaller forecast error than shorter QPF durations only for non-flood forecasts. Experimental results are shown to be consistent with NWS, April 2001 to October 2016, forecast verification statistics for the OHRFC.</p>
Supporting Information for "Geochemistry constrains global hydrology on Early Mars"
<p>Copy of the Supporting Information for "Geochemistry constrains global hydrology on Early Mars", by Edwin S. Kite and Mohit Melwani Daswani.</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
SRTM derived hydrological data for the SAFE Project
<b>Description: </b><p>These files contain flow direction and flow accumulation data for the area surrounding the SAFE Project. The data were derived using compiled Shuttle Radar Topograpy Mission (SRTM) 30m resolution elevation data for the region (<a href="https://zenodo.org/record/630004">https://zenodo.org/record/630004) using the GRASS GIS <code>r.watershed</code> tool.<br><br>Further details of the geoprocessing can be found here: </a><a href="https://www.safeproject.net/dokuwiki/safe_gis/hydrology">https://www.safeproject.net/dokuwiki/safe_gis/hydrology</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3490687">here</a></p><p><b>Files: </b>This dataset consists of 3 files: SAFE_SRTM_hydrology_metadata.xlsx, SRTM_Flow_Direction.tif, SRTM_Log_Flow_Accum.tif</p><p><b>SAFE_SRTM_hydrology_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Flow_Direction.tif</b></p><p>Description: Flow direction data in UTM 50N geotiff</p><p><b>SRTM_Log_Flow_Accum.tif</b></p><p>Description: Flow accumulation data in UTM50N geotiff</p><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</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.