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47 results for “Lakes and reservoirs”
LAGOS-US RESERVOIR: Data module classifying conterminous U.S. lakes 4 hectares and larger as natural lakes or reservoirs
The LAGOS-US RESERVOIR data module (hereafter, RESERVOIR) classifies all 137,465 lakes > 4 hectares in the conterminous U.S. into one of the following three categories using a machine-learning predictive model based on visual interpretation of lake outlines and a classification rule based on lake shape. Natural Lakes (NLs) are defined as lakes that are likely to be entirely or mostly naturally-formed and that do not have large, flow-altering structures on or near them; Reservoir Class A’s (RSVR_A) are defined as lakes that are likely to be either human-made or highly human-altered by the presence of a relatively large water control structure that appears to significantly change the flow of water; and Reservoir Class B’s (RSVR_Bs) are lakes that are likely to be entirely human-made based on isolation from rivers and a highly angular shape that is rarely, if ever, seen in natural lakes also often. We trained the machine learning models on 12,162 manually-classified lakes to assign probabilities of a lake being in 1 of 2 of the categories (NL or RSVR), then we further classified the RSVR classification into either A or B based on NHD Fcodes, isolation, and angularity. The data module includes a detailed User Guide, metadata tables, and a data table that includes information such as location, lake geometry, surface water connectivity class, and official name. Using our definition, our classification indicates that over 46 % of lakes > 4 ha in the conterminous U.S. are reservoir lakes. These data can be combined with other LAGOS-US data modules and U.S. national databases using unique lake identifiers to study both reservoir lakes and natural lakes at broad scales.
Data associated with the FLooded Upland Dynamics EXperiment (FLUDEX), conducted at the IISD Experimental Lakes Area 1997 to 2003, investigating reservoir flooding impacts on ecosystems, particularly the release of mercury and greenhouse gases.
The data included in this repository were collected over the course of the FLooded Upland Dynamics Experiment (FLUDEX) conducted at the IISD Experimental Lakes Area (IISD-ELA) from 1997 to 2003. A plethora of data was collected over five years of flooding three upland reservoir sites, in order to examine the relationship between the amount of flooded, and thus decomposed, terrestrial organic matter and the production of methylmercury (MeHg), total mercury (THg), and greenhouse gases (GHGs) in the reservoirs. Findings from this experiment suggest that the amount of organic carbon stored in a flooded site does not directly influence the amount of THg, MeHg, and GHGs produced, but it does affect the persistence of mercury in the reservoir and food web. This version of the repository contains data collected on water chemistry, benthic invertebrate (chironomid) emergence, mercury and methylmercury concentrations in the water and food web, stable isotopes of carbon and nitrogen in emerging insects and zooplankton, and abundance and biomass of zooplankton, phytoplankton, and bacteria. This data package contains only some of the data from the FLUDEX project. IISD-ELA hopes to add more data in subsequent versions.
Missouri Lakes and Reservoirs Long-term Limnological Dataset, 1976-2018.
This data set compiles 43 years of limnological data from Missouri lakes and reservoirs collected by the University of Missouri Limnology Lab. Although the dataset includes information from nine different projects, the bulk of the data (~75%) come from the Statewide Lake Assessment Project and the Lakes of Missouri Volunteer Program, both of them funded primarily by Missouri Department of Natural Resources. The Statewide Lake Assessment Project began in 1978 sampling a small set of reservoirs. In 1989 the assessment expanded to include regular annual summer monthly collections between May and August, though monitoring was extended for some reservoirs in certain years. We monitored 240 lakes to create the dataset, which represents over 2600 lake-years. The Lakes of Missouri Volunteer Program began in 1992 monitoring 5 lakes and reservoirs and has expanded to 121 sites on 65 waterbodies. Volunteer community scientists monitor their respective sites approximately 8 times per season (April through September). This dataset represents over 15,000 sample events. Lake Ozarks is a long-term (1976-2014) spatial examination of a single large reservoir during summer. Table Rock Monitoring is another multi-year (1995-2009) spatial examination of a large reservoir, but includes year-round data. The rest of the projects included in the dataset monitored Missouri lakes and reservoirs at various intervals including daily (Woodrail, Daily), weekly (icubed), and biweekly (High Res).
Filtered chlorophyll a time series for Beaverdam Reservoir, Carvins Cove Reservoir, Claytor Lake, Falling Creek Reservoir, Gatewood Reservoir, Smith Mountain Lake, Spring Hollow Reservoir in southwestern Virginia, and Lake Sunapee in Sunapee, New Hampshire, USA during 2014-2025
Water column chlorophyll a was analyzed from 2014 to 2025 in seven freshwater reservoirs in southwestern Virginia (VA), USA, and one freshwater lake in central New Hampshire (NH), USA. These waterbodies are: Beaverdam Reservoir (Vinton, VA), Carvins Cove Reservoir (Roanoke, VA), Claytor Lake (Pulaski, VA), Falling Creek Reservoir (Vinton, VA), Gatewood Reservoir (Pulaski, VA), Smith Mountain Lake (Bedford, VA), Spring Hollow Reservoir (Salem, VA), and Lake Sunapee (Sunapee, NH). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia; Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia; and Smith Mountain Lake is jointly treated by the Bedford Regional Water Authority and the Western Virginia Water Authority as a drinking water source for Franklin County, Virginia. Claytor Lake is managed for hydroelectric power generation by the Appalachian Power Company. Lake Sunapee is a glacially-formed lake known for its oligotrophic water quality. The dataset consists of depth profiles of chlorophyll a samples generally measured at the deepest site of each reservoir adjacent to the dam or at the buoy site of Lake Sunapee. The water column samples were collected approximately fortnightly from March-April and weekly from May-October from 2014 - present at Falling Creek Reservoir and Beaverdam Reservoir, approximately fortnightly from May-August in most years at Carvins Cove Reservoir, approximately fortnightly from May-August in Gatewood and Spring Hollow Reservoirs from 2014-2016, approximately fortnightly from May-August of 2014 in Smith Mountain Lake, sporadically from May-August of 2014 in Claytor Lake, and sporadically from June-August of 2021-2022 and 2024-2025 in Lake Sunapee. From 2018-2025, samples were collected primarily at a single depth in each reservoir, with sample collection at two depths in F
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
General Lake Model-Aquatic EcoDynamics model parameter set for Falling Creek Reservoir, Vinton, Virginia, USA 2013-2019
The General Lake Model (GLM), an open-source, one-dimensional hydrodynamic model, was used to simulate physical, chemical, and biological variables in Falling Creek Reservoir, Vinton, Virginia, USA between 15 May 2013 and 31 December 2019. GLM (v.3.2.0a3) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires three configuration files to run the model. First, the glm3.nml file configures lake metadata (including hypsometry), meteorological driver data, stream inflow and outflow driver data files, and physical response variables (mixing parameters and sediment heat zones). Second, the aed2_20220111_2DOCpools.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, silica, nitrogen, phosphorus, organic matter, and phytoplankton. Third, the aed2_phyto_pars_4Jan2022.nml file configures all parameters pertaining to phytoplankton dynamics. Meteorological data, two surface stream inflow files, a submerged oxygenation inflow file, and outflow file used in this calibration are also included.
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Missouri reservoir water quality data (2022 - current) from the Statewide Lake Assessment Program (SLAP)
This dataset of limnological water quality data continues from North et al., 2025, starting in 2022 until present. The data is from reservoirs, primarily within the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: ammonium (NH4), anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, dissolved organic carbon, microcystin, nitrate & nitrite (NO3), pheophytin, particulate inorganic matter, particulate organic matter, phycocyanin, saxitoxin, Secchi disk depth, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids, and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete depths in the hypolimnion.
Long term limnological measurements in Acton Lake, a southwest Ohio reservoir, and its inflow streams: 1992-2023
Long-term data were collected from Acton Lake and its inflow streams on a suite of physical, chemical and biological variables. The data are collected as part of long-term research investigating how Acton Lake, a eutrophic reservoir, responds to changes in ecosystem subsidies of detritus (sediments) and nutrients. Our data span 31 years, from 1992-2023, although for some parameters the data set spans a shorter time frame within this period. In three of Acton Lake’s inflow streams, collectively constituting ~86% of the lake’s watershed, we include data on hourly stream discharge, as well as concentrations of suspended sediments, ammonium-N, nitrate+nitrite-N, and soluble reactive phosphorus (P). Data on the concentrations of these constituents were collected at various time scales depending on stream discharge (usually every 6-8 hours during storms, every 1-3 days during baseflow). In Acton Lake, we collected data on several parameters from an “outflow” site, at the deepest part of the lake where the water column is usually thermally stratified in summer. Vertical profile data were collected for temperature, dissolved oxygen, photosynthetically active radiation (PAR), and chlorophyll. Depth profile data were collected at 0.5 or 1 m intervals (depending on depth and year), usually weekly from April or May until September or October. In addition, data for several parameters were obtained from “integrated samples” that collect water from the lake surface to the bottom of the euphotic zone (defined as the depth at which PAR equals 1% of surface PAR) with a tube or pump. Parameters for which integrated data are presented include chlorophyll; suspended solids; non-volatile suspended solids; particulate (seston) carbon, nitrogen and phosphorus; and total nitrogen and total phosphorus. We also collected Secchi depth data, using standard methods. Larval fish were collected in the top 1-3 meter stratum of the water column with a metered net to generate estimates of lake-wide l
Lake Pertusillo reservoir induced seismicity catalog (Southern Italy)
<p>We present a detailed analysis of the small magnitude (M<sub>L</sub><3) Reservoir Induced Seismicity associated with the Pertusillo water reservoir located in the high seismic hazard zone of Val d’Agri (Southern Italy). </p> <p>We apply template-matching detection to a 13-month-long dense passive survey, obtaining a final high-precision double-difference catalog of 5,068 earthquakes (-0.7<M<sub>L</sub><2.6, M<sub>C</sub>=0.2). </p> <p>The original <em>template</em> dataset is composed of 408 hand-picked earthquakes, recorded during a 13-month-long (2005-2006) passive survey, at a dense network of 46 seismic stations (the average receiver spacing is 5 km) composed by: 22 temporary 3C continuously-recording stations of the temporary experiment described in <em>Valoroso et al.</em>, [2009], plus 24 permanent INGV and ENI (the local oil company) stations, and accurately located in a 3D high-resolution V<sub>P</sub> and V<sub>P</sub>/V<sub>S</sub> velocity model [<em>Improta et al.</em>, 2017]. </p> <p>The attached file is a plain text with a space as separator. </p> <p>Here below the header is explained.</p> <p><strong>ID: </strong>the unique event identifier</p> <p><strong>LAT</strong>: hypocenter latitude expressed in degrees </p> <p><strong>LONG</strong>: hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>DEPTH</strong>: hypocenter depth expressed in km </p> <p><strong>OTIME: </strong>date of the origin time in the format YYYY-MM-DD[T]hh:mm:ss.msec</p> <p><strong>ML</strong>: magnitude (pure number)</p> <p> </p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
Global Lake Ecological Observatory Network: Long term chloride concentration from 529 lakes and reservoirs around North America and Europe: 1940-2016
This dataset compiles long term chloride concentration data from 529 freshwater lakes and reservoirs in Europe and North America. All lakes in the dataset had greater than or equal to ten years of data. For each lake the following landscape and climate metrics were calculated: mean annual precipitation, mean monthly air temperatures, road density and impervious surface in 100 to 1500 m buffer zones, sea salt deposition. The dataset includes three files: 1) Descriptive data of lake sites (physical lake metrics, climate, land-cover characteristics), 2) Chloride time-series, and 3) GIS shapefiles.
Half-hourly gap-filled Northern Hemisphere lake and reservoir carbon flux and micrometeorology, 2006 - 2015
This archive accompanies the manuscript New insights into diel to interannual variation in carbon emissions from lakes and reservoirs We synthesize 171 site-months (and 3,832 site-hours) of high-frequency flux measurements to quantify the magnitudes and temporal variability of direct CO2 fluxes from 13 lakes and reservoirs in the Northern Hemisphere (NH). Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. These data were collected based on a workshop and open call for eddy covariance observations over lakes organized by Ankur Desai (UW-Madison), Timo Vesala (U Helsinki), and Malgorzata Golub (DKIT).
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 2. Места нахождениЯ Amuranodonta kijaensis на территории Хинганского Заповедника, АмурскаЯ обл.: А. Схема расположениЯ лесничеств: 1 – Антоновское, 2 – Лебединское, 3 – Хинганское. B. ТопографическаЯ карта Антоновского вдхр. у пос. Архара. С, D. Топографические карты и спутниковый снимок оЗ. Яценково на территории Антоновского лесничества. E–G. ТопографическаЯ карта и спутниковый снимок оЗ. ПереШеечное на территории Лебединского лесничества. МасШтабные линейки: 20 км (А), 4 км (В, Е), 5 км (С), 1 км (D, G), 2 км (F). FIG. 2. Localities of Amuranodonta kijaensis in the Khingansky Reserve, Amur Region: A. Layout of forestry areas: 1 – Antonovsky, 2 – Lebedinsky, 3 – Khingansky. B. Topographic map of Antonovskoe Reservoir near Arkhara village. C, D. Topographic maps and satellite image of Yatsenkovo lake, Antonovsky forestry. E–G. Topographic map and satellite image of Peresheechnoe lake, Lebedinsky forestry. Scale bars: 20 km (A), 4 km (B, E), 5 km (C), 1 km (D, G), 2 km (F).
РИС. 5. Места находок Amuranodonta kijaensis: A. Зейское вдхр. В–D. Река Амур у с. Чныррах. E, F. ОЗеро ПереШеечное. G. ОЗеро Долгое. H. Антоновское вдхр. FIG. 5. Localities of Amuranodonta kijaensis: A. Zeya Reservoir. B–D. Amur River near Chnyrrakh village. E, F. Peresheechnoe lake. G. Dolgoe lake. H. Antonovskoe Reservoir. in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 5. Места находок Amuranodonta kijaensis: A. Зейское вдхр. В–D. Река Амур у с. Чныррах. E, F. ОЗеро ПереШеечное. G. ОЗеро Долгое. H. Антоновское вдхр. FIG. 5. Localities of Amuranodonta kijaensis: A. Zeya Reservoir. B–D. Amur River near Chnyrrakh village. E, F. Peresheechnoe lake. G. Dolgoe lake. H. Antonovskoe Reservoir.
Surface Water Area Variations of Global Lakes and Reservoirs
<p>Monthly surface area timeseries of large lakes and reservoirs generated from Sentinel-1 SAR backscatter data from January 2017 through December 2019.</p>
Fig. 3 in Dragonfly Assemblages Of A Shallow Lake Type Reservoir (Tisza-Tó, Hungary) And Its Surroundings
Fig. 3. Hierarchical cluster analysis of the water bodies. Rogers–Tanimoto dissimilarity and single average fusion algorithm were used. Notations: 1 = leaking canals, 2 = new inundated area, 3 = native
Fig. 2 in Dragonfly Assemblages Of A Shallow Lake Type Reservoir (Tisza-Tó, Hungary) And Its Surroundings
Fig. 2. Diversity profiles of the studied water bodies. Notations: = leaking canals, = new inundated area, ¨ = native water bodies, = in- and outflows, = River Tisza
Fig. 1 in Dragonfly Assemblages Of A Shallow Lake Type Reservoir (Tisza-Tó, Hungary) And Its Surroundings
Fig. 1. Species richness of the dragonfly fauna of the water bodies, separately for the two suborders. Notations: 1 = leaking canals, 2 = new inundated area, 3 = native water bodies, 4 = in- and outflows,
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.