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182 results for “Hydrological Data”
The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Jobos Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six mangrove forest locations in Jobos Bay National Estuarine Research Reserve (JBNERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Maria (September 2017) were identified and selected for hydrologic monitoring coincident with sites sampled for structural metrics in 2022. One reference site, defined as a site that was observed to be recovering following the hurricane, was selected in black mangrove forest. Two of the six sampling locations were selected to monitor effects of human encroachment on the western boundary of the reserve. These two sites were not coincident with structural sampling plots established in 2022. This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
Mangrove Coast Collaborative Project, Hydrologic monitoring data in mangrove forests, Rookery Bay NERR, April 2024 - December 2024
The dataset describes the hydrologic conditions of the soil porewater (water level, conductivity, and temperature) at a depth of ~70 cm below ground in six black mangrove forest locations in Rookery Bay National Esturarine Research Reserve (NERR) at 30-minute intervals between April 2024 to December 2024. Locations of minimal forest recovery following the effects of Hurricane Irma (September 2017) were identified and selected for hydrologic monitoring. The design consists of three sites in mainland/interior black mangroves and three sites on ocean-facing islands, all of which are located on the east side of Hurricane Irma eyewall. In each group, two of the sites selected were considered sites of minimal recovery whereas one site was selected as a reference (location of recovering mangroves). This dataset is associated with the MCC Catalyst Project entitled Limits of Resilience (2023-2025) funded by the National Estuarine Research Reserve System (NERRS) Science Collaborative.
2017 hydrologic, water quality, and soil quality data from The Jefferson Projects 8 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had eight tributary monitoring stations around the lake collecting data on water quality, soil quality and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_ShelvingRock and TS_West. The stations have a sensor payload that may include some or all of the following sensors: EXO2 Multi-parameter sonde, CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter with five 3.0 MHz transducers, Argonaut-SL Doppler current meter, WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and down sampling to an hourly frequency.
Graph Data: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios
<p>Data used for creating the figures in the paper: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios.</p> <p>It contains the flow exceedances (as mm day<sup>-1</sup>), flow duration slope, median elasticity and runoff ratio for the different afforestation scenarios. Also included is the information on the changes of broadleaf afforestation. </p> <p>If you have any questions, please email marcus.buechel@ouce.ox.ac.uk.</p>
Hydrological, physicochemical and metabolic activity data for streams in the Japanese Alps
<p>A series of files with hydrological, physicochemical and metabolic activity data from a study investigating the environmental dynamics of six stream systems in the Japanese Alps.</p>
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
The Jefferson Project 2021 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
Physical Hydrologic Data for the National Audubon Society's 16 Research Sites in coastal mangrove transition zone of southern Florida, March 1986 - ongoing
Temperature, salinity and depth were continuously collected using Hydrolab/Hach sensors within the coastal mangrove transition zone at 16 sites from southern Biscayne Bay to Cape Sable. Data were collected at 12 sites within the coastal mangrove zone of Everglades National Park, incorporating the Cape Sable, Taylor River and Panhandle region. Data were collected at 4 sites within the coastal mangrove zone of southern Biscayne Bay, incorporating the Manatee Bay, Barnes Sound, and Card Sound regions. Rainfall, pH, and dissolved oxygen were collected at a number of these sites with varying periods of record.
Periphyton, hydrological and environmental data in a coastal freshwater wetland (FCE), Florida Everglades National Park, USA (2014-2015)
The characteristic, calcareous periphyton mats of the Everglades, and particularly their diatom assemblages, provide an ideal community to study the patterns and mechanisms of community assembly along environmental gradients with ecotones. Understanding patterns and mechanisms of diatom community assembly along salinity and P gradients can be incorporated into tools for predicting changes in these gradients, and the location and movement of the "white zone" ecotone, caused by saltwater intrusion and water management outcomes in the Southern Everglades. Patterns of environmental variation and periphytic-diatom community structure along the freshwater-marine gradient of Everglades National Park, FL., USA were examined by sampling along a series of 7 transects extending from oligotrophic, freshwater marshes through the ecotone and down to the northern edge of the fringing mangrove forests. Seven transects spanning the west-east extent of the southeast Everglades, from the Main Park Road in the west to the Model Lands in the east, were sampled once in the dry season (May) and once in the wet season (November) of 2014 and 2015. These data are published in "Mazzei and Gaiser. 2018. Diatoms as tools for inferring ecotone boundaries in a coastal freshwater wetland threatened by saltwater intrusion. Ecological Indicators. 88:190-204."
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Hydrology-mediated ecological function of a large wetland threatened by an invasive predator"
Asian swamp eels (Monopterus albus/javanensis complex) were first reported from Florida in 1997 and the Everglades in 2007; swamp eels have been established in Taylor Slough of Everglades National Park since 2014. This dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 24 sites across four regions of the Everglades: Taylor Slough, Shark River Slough, Water Conservation Area 3, and the C-111 Panhandle. Prey species included are the six most common small fishes prior to swamp eel invasion of Taylor Slough (1996–2009) and the three common decapod species (two crayfish species and grass shrimp). An annual index of mean wet season electrofishing catch-per-unit-effort of swamp eels, Mayan cichlids (Mayaheros uruphthalmus), and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) is included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses are included.
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Summer and winter invertebrate and physicochemical data from the Coweeta Hydrologic Lab
<p>This resource contains data for aquatic invertebrates collected from leaf litterbags, which were deployed in 11 streams at the Coweeta Hydrologic Lab (Macon County, North Carolina, USA) during winter and summer months in 2017-2018. Litterbags consisted of fine-mesh bags (250µm) attached to coarse-mesh bags (5mm), each containing <em>Rhododendron maximum</em> leaf litter. The litterbags were deployed for two-month periods, which were as follows: 19 October - 11 or 18 December, 2017; 15 November - 5 January 2017-2018; 9 May - 5 July 2018; and 5 July - 31 August 2018. We collected invertebrate samples from the >1mm size fraction from 3 coarse-fine litterbag pairs incubated in each of our streams during the aforementioned 2-month periods. Invertebrates were preserved in ethanol, identified, and classified into functional feeding groups based on classifications in Merritt et al. 2019. We identified invertebrates in the "shredder" functional feeding group to genus and all other insects to family (Merritt et al. 2019). We also measured invertebrate lengths in mm and converted these lengths to masses using information from Benke 1999. This resource also contains daily temperature and discharge data, litter breakdown data from the coarse-mesh bags associated with the invertebrate data, and weekly nutrient concentration data from the streams during the study period. Discharge data was provided by the USFS Coweeta Hydrologic Lab and can also be found here: https://www.fs.usda.gov/rds/archive/catalog/RDS-2016-0025-2</p> <p>Discharge data citation:</p> <p>USFS Coweeta Hydrologic Laboratory. 2023. Daily streamflow data for gauged watersheds at Coweeta Hydrologic Laboratory, North Carolina. 2nd Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2016-0025-2</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Rosalia: An experimental research site to study hydrological processes in a forest catchment - data repository
<p>This repository is a supplement to the paper <strong>Fürst, J., et al. (2021). “Rosalia: an experimental research site to study hydrological processes in a forest catchment.” Earth Syst. Sci. Data 13(8): 4019-4034.</strong></p> <p>Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19<sup>th</sup> and early 20<sup>th</sup> century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called “Rosalia” with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the “Rosalia” instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum.</p> <p>This repository contains the datasets collected by a monitoring network of 4 discharge gauging stations, 7 rain-gauges, together with observations of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community.</p>
Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain: open data and code
<p>Quality controlled temperature data at daily resolution at CTS, HRR, HYS, NVC, RSI and SGV and the most relevant codes for data processing used in:</p> <p>García-Pereira, F., González-Rouco, J. F., Schmid, T., Melo-Aguilar, C, Vegas-Cañas, C., Steinert, N. J., Roldán-Gómez, P. J., Cuesta-Valero, F. J., García-García, A., Beltrami, H., and de Vrese, H.: "Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain". Earth Surf. Dynam., submitted, 2023.</p> <p>All data can be also freely obtained for research from the original data sources, GuMNet (https://www.ucm.es/gumnet/) and AEMET (https://www.aemet.es/en/datos_abiertos). Further details of the code are available upon request to the corresponding author (Félix García-Pereira, felgar03@ucm.es).</p>
Accompanying data for the open-source book Modeling of Hydrological Systems in Semi-Arid Central Asia
<p>This data set is used to reproduce examples in the open-source book <a href="https://hydrosolutions.github.io/caham_book/">"Modeling of Hydrological Systems in Semi-Arid Central Asia"</a> which is part of a free course on hydrological modeling in Central Asia. The course teaches how to use publicly available data to implement a hydrological model for climate impact studies (Marti et al., 2023). </p> <p>To use the data set to reproduce the examples in the book: Download the book from https://doi.org/10.5281/zenodo.6350042 and this data set to the same hierarchical level in your file system: </p> <p>|- caham_book<br> |- caham_data<br> |- AmuDarya<br> |- central_asia_domain<br> |- student_case_study_basins<br> |- SyrDarya</p> <p>You will need a working installation of R (https://www.r-project.org/) and a GUI (e.g. Posit, formerly RStudio https://posit.co/) to reproduce the scripted examples in the book. Once your software is set up, you can proceed to run the examples. </p> <p> </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.