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59 results for “river surface”
РИС. 7. НедостаточнаЯ промывка раковин глохидиев после очиЩениЯ в Щелочи (5% КОН). А, С. «Замыленность» пор наружной поверхности створок (Cristaria tuberculata, оЗ. Ханка, Приморский кр.). B. Остаток Щелочи, выпавШий кристаллами на поверхности личинки (Unio dembeae, р. Дуко, ЭфиопиЯ). D. Капли раствора Щелочи (укаЗаны стрелками) на поверхности Шипов крючка (Nodularia douglasiae, р. Гион, о-в Хонсю, ЯпониЯ). МасШтаб 5 мкм (А, С), 2 мкм (B, D). Микроскоп Zeiss MERLIN, напыление углеродом (А, В), хромом (С, D). FIG. 7. Insufficient rinsing of glochidia after cleaning in alkali (5% KOH). A, C. «Blurredness» of the exterior valve pores (Cristaria tuberculata, Khanka Lake, Primorsky Krai). B. Precipitation of alkali crystals on the exterior glochidia surface (Unio dembeae, Duko River, Ethiopia). D. Drops of alkali (indicated by arrows) on the hook spines (Nodularia douglasiae, Gion River, Honshu Island, Japan). Scale bars 5 μm (A, C), 2 μm (B, D). Zeiss MERLIN microscope, sputter coating with carbon (A, B) and chromium (C, D). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 7. НедостаточнаЯ промывка раковин глохидиев после очиЩениЯ в Щелочи (5% КОН). А, С. «Замыленность» пор наружной поверхности створок (Cristaria tuberculata, оЗ. Ханка, Приморский кр.). B. Остаток Щелочи, выпавШий кристаллами на поверхности личинки (Unio dembeae, р. Дуко, ЭфиопиЯ). D. Капли раствора Щелочи (укаЗаны стрелками) на поверхности Шипов крючка (Nodularia douglasiae, р. Гион, о-в Хонсю, ЯпониЯ). МасШтаб 5 мкм (А, С), 2 мкм (B, D). Микроскоп Zeiss MERLIN, напыление углеродом (А, В), хромом (С, D). FIG. 7. Insufficient rinsing of glochidia after cleaning in alkali (5% KOH). A, C. «Blurredness» of the exterior valve pores (Cristaria tuberculata, Khanka Lake, Primorsky Krai). B. Precipitation of alkali crystals on the exterior glochidia surface (Unio dembeae, Duko River, Ethiopia). D. Drops of alkali (indicated by arrows) on the hook spines (Nodularia douglasiae, Gion River, Honshu Island, Japan). Scale bars 5 μm (A, C), 2 μm (B, D). Zeiss MERLIN microscope, sputter coating with carbon (A, B) and chromium (C, D).
РИС. 9. ВнеШний вид раковин глохидиев (Sinanodonta woodiana, р. Одра, ПольШа), очиЩенных с помоЩью Щелочи (5% KOH). A. Темные пЯтна на поверхности, свидетельствуюЩие о недостаточной промывке после Щелочи. B. ХороШо очиЩеннаЯ и правильно промытаЯ раковина. МасШтаб 50 мкм. Микроскоп Zeiss EVO 40, напыление Золотом. FIG. 9. Glochidia shells (Sinanodonta woodiana, Odra River, Poland) cleaned in alkali (5% KOH). A. Dark spots on the shell surface, indicating insufficient rinsing after alkali. B. Properly cleaned and rinsed shell. Scale bars 50 μm. Zeiss EVO 40 microscope, sputter coating with gold. in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 9. ВнеШний вид раковин глохидиев (Sinanodonta woodiana, р. Одра, ПольШа), очиЩенных с помоЩью Щелочи (5% KOH). A. Темные пЯтна на поверхности, свидетельствуюЩие о недостаточной промывке после Щелочи. B. ХороШо очиЩеннаЯ и правильно промытаЯ раковина. МасШтаб 50 мкм. Микроскоп Zeiss EVO 40, напыление Золотом. FIG. 9. Glochidia shells (Sinanodonta woodiana, Odra River, Poland) cleaned in alkali (5% KOH). A. Dark spots on the shell surface, indicating insufficient rinsing after alkali. B. Properly cleaned and rinsed shell. Scale bars 50 μm. Zeiss EVO 40 microscope, sputter coating with gold.
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E). in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 5. ЗагрЯЗнение готовых обраЗцов длЯ СЭМ при длительном хранении в негерметичных условиЯх (A–C) либо при хранении проШедШих процедуру мацерированиЯ беЗ последуюЩего обеЗЗараживаниЯ (D, E). A–С. Бактерии на поверхности глохидиев (Nodularia douglasiae, р. ИлистаЯ, бассейн оЗ. Ханка, Приморский кр.). А. ВнеШний вид глохидиЯ, основное ЗагрЯЗнение на створке в верхней части фото. В. Крючок глохидиЯ, основное ЗагрЯЗнение в левой части фото. С. Створка, вид иЗнутри. D. Единичные бактерии на створке глохидиЯ, вид иЗнутри (Kunashiria japonica, оЗ. Утиное, о-в Зелёный, Курильские о-ва). E. Гифы гриба на створке глохидиЯ, вид на наружную пору (Beringiana beringiana, оЗ. АЗабачье, Камчатка). МасШтаб 50 мкм (А, C), 10 мкм (В, D), 1 мкм (Е). Микроскопы Zeiss MERLIN (А, B, C, E), Zeiss EVO 40 (D), напыление хромом (А–С), Золотом (D), углеродом (Е). FIG. 5. Contamination of the SEM ready-made samples during long-term storage under unsealed conditions (A–C) or during storage the samples that have passed the maceration procedure without subsequent disinfection (D, E). A–C. Bacteria on the glochidia surface (Nodularia douglasiae, Ilistaya River, Khanka Lake basin, Primorsky Krai). A. Glochidium with the main pollution on the valve in the upper part of the photo. B. Hook with the main pollution on the left side of the photo. C. Interior valve. D. Bacteria on the interior valve (Kunashiria japonica, Utinoe Lake, Zeliony Island, Kuril Islands). E. Fungal hyphae on the pore of exterior valve (Beringiana beringiana, Azabachye Lake, Kamchatka). Scale bars 50 μm (A, C), 10 μm (B, D), 1 μm (E). Zeiss MERLIN (A, B, C, E) and Zeiss EVO 40 (D) microscopes, sputter coating with chromium (A–C), gold (D), and carbon (E).
Time series of electrical conductivity, temperature and relative stream stage recorded in surface water and streambed sediments of River Erpe and River Gruendlach, Germany
<p><span><a href="../api/records/13336325/draft/files/temp_EC_timeseries.csv/content" target="_blank" rel="noopener noreferrer">temp_EC_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC) and relative stream stage (cm) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Gruendlach, Germany.</p> <p> </p> <p><span><a href="../api/records/13336325/draft/files/porewater_ec_timeseries.csv/content" target="_blank" rel="noopener noreferrer">porewater_ec_timeseries.csv</a></span>: Time series of electrical conductivity (mS cm<sup>-1</sup>), temperature (degC), relative stream stage (cm) and total pressure (hPa) recorded in the surface water and in streambed sediments (depth in cm) of River Erpe and River Ammer, Germany, and the Sturt River, South Australia.</p>
Five day averaged sea surface salinity from MITgcm integration with climatological river discharge forcing
<p>The 5-day averaged sea surface salinity for experiment using climate river discharge, with file names like sss_clmflux_YEAR.mat for each year</p>
Five day averaged sea surface height from MITgcm integration with climatological river discharge forcing
<p>The 5-day averaged sea surface height for experiment using climate river discharge, with file names like ssh_dayflux_YEAR.mat for each year.</p>
Five day averaged sea surface salinity from MITgcm integration with daily river discharge forcing
<p>The 5-day averaged sea surface salinity (SSS) for experiment using daily river discharge, with file names like sss_dayflux_YEAR.mat for each year (1992-2017)</p>
Five day averaged sea surface height from MITgcm integration with daily river discharge forcing
<p>The 5-day averaged sea surface height (SSH) for experiment using daily discharge, with file names like ssh_dayflux_YEAR.mat for each year</p>
Temporal trends of surface water area in India's rivers and basins
<p>This dataset quantifies the extent and annual rate of change in surface water area (SWA) in India's rivers and basins over a period of 30 years from 1991 to 2020. Visit <a title="Surface Water Trends - India" href="https://sites.google.com/view/surface-water-trends-india/" target="_blank" rel="noopener">Surface Water Trends - India</a> for an interactive web interface to explore these results, and for additional data and information.</p> <p>It is derived from the <a href="https://global-surface-water.appspot.com/" target="_blank" rel="noopener">Global Surface Water Explorer</a> which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.</p> <p>Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.</p> <p>CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.</p> <p>Refer to 00_README.txt for details on feature and table attributes in the files. </p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Surface Area of River and Lakes (SARL)
<p>The Surface Area of River and Lakes (SARL) dataset has been developed to show the 38-years of seasonal and permanent water surface area change in rivers and lakes.</p>
Napa River Watershed, U.S.A. soil leachate and surface water sulfate sulfur isotopes and concentrations
Sulfur (S) is widely used in agriculture, yet little is known about its fates and consequences within upland, mixed land-use land-cover watersheds. This dataset includes samples collected throughout the Napa River Watershed, California, U.S.A., where high S applications to vineyard agriculture are common. Sample collection focused on tracing the agricultural S “fingerprint” —or the combined S stable isotope composition and concentration of sulfate—through the Watershed. We collected samples during California wet seasons (December – April) over three years (2017-2020). Samples included vineyard agriculture and non-agricultural (primarily forest, grassland, and shrubland) soil water, culvert outflows, tributaries to the Napa River, and Napa River surface water. The data table includes water sample sulfate concentrations and sulfate-S stable isotope measurements as well as the stable isotope composition of S fungicide samples.
Surface and hyporheic water chemistry of the Tanana River
The geochemistry of hyporheic water at two islands on the Tanana River. The chemistry of wells was sampled twice a month and analyzed for major solutes and dissolved gases. Samples were analyzed for Ca, Mg, Na, K, NH4, Cl, NO3, SO4, DOC, TDN, CH4, CO2, N2O, pH and conductivity.
Change in marsh surface elevation measured with a Surface Elevation Table (SET) at control plots in a Spartina alterniflora-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh platform at a Spartina alterniflora-dominated marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site, MA.
Change in marsh surface elevation measured with a Surface Elevation Table (SET) at control plots in a Spartina patens-dominated salt marsh at Law's Point, Rowley River, Plum Island Ecosystem LTER, MA.
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh platform at a Spartina patens-dominated marsh on the Rowley River in the Plum Island Ecosystem (PIE) LTER site, MA.
Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers"
<p>Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers", whose manuscript will be submitted by 10/25/2023</p> <p><br>The dataset contains the necessary data to generate the figures in the paper with the code in the link <a href="https://doi.org/10.5281/zenodo.10958491">https://doi.org/10.5281/zenodo.10958491</a> whose Github reference is <a href="https://github.com/meteorologytoday/paperfigures-2023-AR-SST-response">https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response</a></p> <p> </p>
Surface flow velocity from Pulmanki, Koita and Sävar Rivers 2020-2022
<p>Data description:<br>Surface flow velocity dataset was created using Hydro-STIV software (Hydro-STIV v.1.2.2, Hydro Technology Institute co.), which uses Space-Time Image Velocimetry (STIV) for velocity estimation, a method derived from Large-Scale Particle Image Velocimetry (LSPIV) developed by Fujita in 2007 (Fujita et al., 2007). The videos were georeferenced using known GCPs and the software performed orthorectification and calibration. The data has been used for publication "Surface flow and ice rafting velocities during freezing and thawing periods in Nordic rivers". Data consists of videos and daily stil images from Pulmanki, Koita and Sävar Rivers from Autumn freezing and Spring thawing periods. The data from Koita River is from 2020-2021 and from Pulmanki and Sävar Rivers from 2021-2022. The original raw data based on which the STIV analysis was performed was collected with Burrel time-lapse RGB cameras. </p> <p> </p> <p>Acknowledgements:<br><span>The river-ice related measurements were initiated at Pulmankijoki River in 2014 under the post-doctoral research project of Dr Lotsari, funded by the Research Council of Finland (ExRIVER: grant number 267345), and this study is a continuum in the series of these winter season studies. The work for this study was financially supported by four other projects funded by the Research Council of Finland (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394; Digital Waters [DIWA] Flagship;359248). In addition, the work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). The Department of Geographical and Historical Studies, University of Eastern Finland, supported financially the field work done at Koita River. The work by Dr Lina Polvi-Sjöberg at the Sävar River was financed by a grant (2023-01513) from the Swedish Research Council Formas.</span></p>
Sequences of Orthorectified Images of the Water Surface of Two Rivers: River Sheaf (Sheffield, UK), and River Calder (Todmorden, UK)
<p>This data set contains sequences of orthorectified images of the free surface of two rivers: </p> <ul> <li>River Sheaf, Sheffield, United Kingdom (Latitude: 53.373056° Longitude: -1.463913° (WGS 84)), recorded between January 2019 and February 2020;</li> <li>River Calder, Todmorden, United Kingdom (Latitude: 53.716198° Longitude: -2.097028°), recorded between 6 and 13 October 2020.</li> </ul> <p>The images are extracted from videos of the water surface also included in the data set. They are complemented by Matlab files containing the corresponding average space-time Fourier spectra. Each measurement is associated with the corresponding measurement of the river depth and estimates of the discharge.</p> <p>Please refer to Documentation.pdf for further details.</p>
Supporting dataset: Towards the Optimal Representation of Sub-Grid Heterogeneity in Land Surface Models - Upper Colorado River Basin study case
<p>Supporting files for the manuscript "Towards the Optimal Representation of Sub-Grid Heterogeneity in Land Surface Models - Upper Colorado River Basin study case." The dataset contains the ESCF derived from the original 800 HydroBlocks simulations used to train/test the random forest models (RFM) to compute the annual mean ESCFs for soil moisture content, sensible heat, latent heat, and runoff, as well as the obtained RFM to compute the resulting number of tiles for a given configuration over the study domain. Besides, the dataset includes the ESCFs for the quasi-fully distributed simulation (QFD) and the input data layers required to run HydroBlocks simulations over the study domain.</p>
Geospatial data used in "Estimation of river water surface elevation using UAV photogrammetry and machine learning"
<p>Geospatial data used in article "Estimation of river water surface elevation using UAV photogrammetry and machine learning" by Radosław Szostak, Marcin Pietroń, Przemysław Wachniew, Mirosław Zimnoch and Paweł Ćwiąkała (AGH UST).</p> <p>Each zip archive contains the following files:</p> <ul> <li>dsm.tif - raster of digital surface model,</li> <li>ortho.tif - raster of orthophoto,</li> <li>gnss_wse.json - geojson multipoint shape containing RTN GNSS measurements of water surface elevation,</li> <li>grid.json - geojson multipolygon shape containing square areas of samples used in deep learning solution.</li> <li>centerline.json - geojson multipoint shape containing values sampled from DSM along centerline,</li> <li>wateredge.json - geojson multipoint shape containing values sampled from DSM along "water-edge".</li> </ul> <p>Data in AMO18.zip archive was collected by Bandini et. al (https://doi.org/10.5281/zenodo.3519888).</p> <p>Preprocessed machine learning dataset and source codes are available in github repository at: https://github.com/radekszostak/river-wse-uav-ml</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.