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12,346 results for “derivatives”
Simulated metagenomes with quality and abundance distributions derived from real samples
<p>Species abundances and quality values were derived from the following list of samples:</p> <pre><code>SAMEA2466896 SAMEA2466916 SAMEA2466952 SAMEA2466953 SAMEA2466965 SAMEA2466996 SAMEA2467015 SAMEA2467039 SAMEA2621010 SAMEA2621033 SAMEA2621107 SAMEA2621155 SAMEA2621229 SAMEA2621247 SAMEA2621300 SAMEA2622357 </code></pre> <p>Reference abundances (.abund files) were generated using <a href="https://github.com/motu-tool/mOTUs_v2">mOTUs profiler</a>.<br> Metagenomes were simulated with <a href="https://sourceforge.net/projects/cmessi/">cMESSi</a> using <a href="http://progenomes.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> for species and the aforementioned abundances. In cases where a <em>ref_mOTU_v2</em> corresponded to more than one genome, the abundance of said <em>ref_mOTU</em> was distributed equally over all genomes.<br> GFF location files were produced using location information generated by cMESSi.<br> Two variants of truth values were obtained by intersecting coordinates of simulated reads with coordinates of <a href="http://eggnogdb.embl.de">eggNOG</a> orthologous groups (OG at NOG level) as predicted by <a href="https://github.com/jhcepas/eggnog-mapper">eggNOG-mapper</a>.</p> <ol> <li>.cog-simulated files contain the NOG distribution that was effectively simulated, <em>i.e.</em> a count of the number of reads overlapping with genes annotated with each NOG. A read overlapping multiple genes is considered for each gene. If a gene possesses multiple NOG annotations, each annotation gets assigned the total number of overlapping reads. Longer genes will (in expectation) generate more reads, all else being equal.</li> <li>.cog-distribution file contains the expected distribution for every NOG on all samples. The number of genes annotated with each NOG is multiplied by the abundance of the corresponding species. Length of the gene is not taken into account.</li> </ol> <p>If you use this dataset, please cite: <a href="https://www.biorxiv.org/node/111718.full">NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language</a></p>
Dataset of "Hydrogen Evolution Reaction Activity in Mo₂TiC₂Tₓ MXene Derived from Mo₂TiAlC₂ MAX Phase: Insights from Compositional Transformations"
<p>MAX phases represent a crucial building block for the synthesis of MXenes, which constitute an intriguing class of materials with significant application potential. This study investigates the catalytic properties of Mo₂TiAlC₂ MAX phase and the corresponding Mo₂TiC₂Tₓ MXene for hydrogen evolution reaction (HER). Characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) revealed that despite the presence of secondary phases, the HER catalytic activity is primarily influenced by the MAX phase and its derived MXene. Interestingly, the catalytic activity of the MXene improves over time, attributed to the formation of MoO₂ as identified by XPS. This work enhances the understanding of MXene-based materials for electrochemical applications, highlighting crucial structural and chemical transformations that optimize their performance in sustainable energy technologies.3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Gas exchange velocities (k600), gas exchange rates (K600), and hydraulic geometries for streams and rivers derived from the NEON Reaeration field and lab collection data product (DP1.20190.001)
This dataset contains estimates of gas exchange velocity, gas exchange rate, and hydraulic parameters for streams calculated from tracer-gas experiments and conservative tracer injections collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, the NEON Reaeration field and lab collection data product (DP1.20190.001) was used to calculate these estimates. Gas exchange was estimated in two ways: first, following an unpooled frequentist approach and second, following a partially pooled Bayesian approach. In addition, a salt-correction was applied to gas exchange estimates for sites where it was possible and necessary. All estimates of gas exchange are included in the file gasExchange_ds.csv. A recommended selection of these estimates is included in the dataset (best_k600_mPerDay and best_K600_mPerDay). The stanfit objects used for the partially pooled Bayesian approach are also included as site-specific model objects for gas exchange velocities and rates. In addition, water velocity was calculated from conservative tracer injections, and mean water depth was calculated from these water velocity estimates and measurements of wetted width and water discharge. All hydraulic parameters are included in the file hydraulics_ds.csv. All processing code is available in the reaRates R package. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Species-level estimated abundances and zero counts of nighttime collected female mosquitoes 2014 - 2022 (Derived from NEON Mosquitoes sampled from CO2 traps (DP1.10043.001, RELEASE-2024))
This Level 2 data package contains species level estimated abundances, including zero counts, and estimated mean number of female mosquitoes per trap derived from the NEON Mosquitoes sampled from CO2 traps (DP1.10043.001), RELEASE-2024 Level 0 data (https://doi.org/10.48443/3cyq-6v47). The data set includes mosquito records of traps collecting mosquito samples at night, for up to 24 trap hours, across a total of 20 terrestrial core and 27 terrestrial gradient sites from 2014 to 2022. To ensure high confidence in abundance estimates, records were only included when at least 90% of collected individuals were identified to sex, and 90% of female specimens were identified to species. Information across multiple QC/QA fields within the NEON mosquito data was evaluated to identify and exclude records where confidence in estimated abundances may have been compromised. Species level zero counts were added for all species collected at least once within the sampling year and trap location. Additionally, species level zero counts were included for trap events where only male mosquitoes had been collected or where QC/QA remarks indicated traps were inactive due to cold temperatures. The data set provides an analysis ready time series of estimated abundances across NEON sites and plots. An R Markdown file that contains descriptions of the QC/QA and data filtering steps along with annotated code, as well as data tables used to filter active and inactive trap events based on QC/QA fields, are published with the data package. Any questions about this data package should be directed to Amely Bauer listed under contacts.
Alteromonas macleodii MIT1002 growth on and uptake of Prochlorococcus-derived metabolites
This data package contains the results from a series of experiments designed to test the response of a heterotrophic, copiotrophic, gammaproteobacterium, Alteromonas macleodii strain MIT1002, to a range of metabolites released by the phytoplankton Prochlorococcus. A. macleodii MIT1002 was isolated from co-culture with Prochlorococcus, and so we hypothesized that A. macleodii MIT1002 would be able to grow on the full range of substrates tested. Instead, we found that A. macleodii MIT1002 could only grow on a narrow range of substrates, and data suggest that this substrate specificity may be related to transporter specificity. We performed two types of experiments: growth experiments and uptake experiments. Data from growth experiments are labeled with the name of the substrate being tested (e.g., “Leu,” for leucine, or “3m2ob”, for 3-methyl-2-oxobutanoic acid). For these experiments, we grew A. macleodii MIT1002 on either pyruvate (as a positive control), a selected Prochlorococcus-related substrate, or a mix of pyruvate and the metabolite. We measured growth in 96-well plates by OD600 using a plate reader which took a measurement every 0.5h for 48h. Uptake experiments are labeled with either “KHU7” (an experiment which tested A. macleodii MIT1002 growth on and uptake of 3-methyl-2-oxobutanoic acid) or “KHU8” (an experiment which tested the A macleodii MIT1002 growth on and uptake of 3-methyl-2-oxopentanoic acid -or lack thereof). For these experiments, we measured growth by flow cytometry. We measured the change in dissolved (i.e., extracellular) metabolite concentration by filtering samples, extracting organic carbon from the filtrate by solid phase extraction, and quantifying selected metabolites from the filtrate by targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). This data package includes the peak areas for targeted metabolites generated by LC-MS/MS, the peak areas for our standard curves used for quantification, and the dissolved metabolite
Dissolved greenhouse gas concentrations derived from the NEON dissolved gases in surface water data product (DP1.20097.001)
This dataset contains partial pressure and molar concentration of dissolved carbon dioxide, methane, and nitrous oxide in 34 streams, rivers, and lakes calculated from headspace equilibration samples collected by the National Ecological Observatory Network (NEON). All input data were collected by NEON and is available on the NEON data portal at https://data.neonscience.org. Specifically, in situ dissolved gas concentrations were calculated from the air and headspace mixing ratios provided by the NEON Dissolved gases in surface water data product (DP1.20097.001), adjusted for sample and water temperature (DP1.20097.001, DP1.20264.001, DP1.20053.001), barometric pressure (DP1.20097.001, DP1.00004.001), and alkalinity (DP1.20093.001). The final set of inputs is found in the file, input_file, and the processing scripts are available at https://github.com/kellyaho/NEON-GHG-processing. The file, output_file, contains the raw outputs from running the input_file through the processing scripts. There are three outputs for each gas for each sample, one for each of three different pre-equilibration headspace mixing ratios (paired atmospheric samples, loess smoothing of atmospheric samples, and site-specific median). The file, GHG_final, contains the final dataset. This GHG_final uses the outputs from output_file calculated with paired atmospheric samples, and substitutes 0.01 μatm, 0.001 μM, 0.001 μatm, and 0.001 μM for any negative instances of pCH4, [CH4], pN2O, and [N2O], respectively. See methods for more detail. Please cite the NEON data inputs (listed below), in addition to this dataset, when using the data. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program.
Satellite-derived estimate of artificial lakes in the Phoenix, Arizona, USA Metropolitan area (2000)
Artificial lakes are a prominent example of a local hydrogeomorphic modification in the Phoenix, Arizona metropolitan area. Although lakes are not a natural feature of Sonoran desert ecosystems, numerous artificial lakes are evident in the region. Here we attempt to quantify the heretofore unknown number and extent of artificial lakes. As official GIS layers from local and state agencies had significant misclassifications and omissions, we used two published land cover datasets to estimate the number and areal extent of artificial lakes. We discovered that there are 908-1,390 lakes in the Phoenix area, with the number varying according to level of aggregation. Accurate data on the extent and distribution of these designed ecosystems is vital for water-resources planning and stormwater management. Results, discussion, and additional details are available in document DOI 10.1007/s11252-011-0208-1.
Composited land surface temperature of the greater Phoenix, Arizona, USA metropolitan area and surrounding Sonoran desert derived from cloud-free, summer (June, July, and August) Landsat imagery: 1985-2020
This project calculates land surface temperature (LST) from remotely sensed imagery. The intent is to extend the previous version of the LST data for the CAP LTER study area in central Arizona, USA to include 2020 and update the products so that they are based on a composite of images from each year (all available cloud-free acquisitions from June, July, and August) in the analysis to reduce the potential for outlier images or pixels to impact analyses. The aim is to make updated LST data accessible to stakeholders and researchers studying the greater Phoenix, Arizona, USA metropolitan area. LST is calculated from cloud-free Landsat 5 and 8 imagery (30m resolution) from summer months (June, July, and August) in 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020. All images are cropped to the CAP LTER study area boundary.
Normalized Difference Vegetation Index (NDVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include NDVI data with SAVI data presented in a companion dataset that is also available through the EDI.
Soil-Adjusted Vegetation Index (SAVI) derived from 2021 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates two vegetation indices —Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI)— from the National Agriculture Imagery Program (NAIP) remotely sensed imagery. The intent is to make remotely sensed variables and visualizations accessible to stakeholders and researchers studying the Phoenix metropolitan area. NDVI and SAVI are calculated from the 2021 NAIP imagery (1m resolution). This dataset extends the 2010, 2013, 2015, 2017, and 2019 NDVI and SAVI products derived from NAIP imagery (also 1m resolution). All images are cropped to the CAP LTER study area boundary of central Arizona, USA. The materials presented here include SAVI data with NDVI data presented in a companion dataset that is also available through the EDI.
Seasonal and annual summary statistics of urbanization, vegetation, land surface temperature, and bioclimatic variables derived from remotely-sensed imagery in areas surrounding long-term bird monitoring locations in the greater Phoenix, Arizona, USA metropolitan area (1997-2023)
This data package consists of 26 years (1998-2023) of environmental data and 22 years (2000-2022) years of bioclimatic data associated with CAP-LTER long-term point-count bird censusing sites (https://doi.org/10.6073/pasta/4777d7f0a899f506d6d4f9b5d535ba09), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). The environmental variables include land surface temperature (LST), three spectral indices of vegetation and water – the normalized difference vegetation index (NDVI), the soil adjusted vegetation index (SAVI), and modified normalized difference water index (MNDWI) – and four spectral indices of impervious surface/urbanization. Impervious surface indices include the normalized difference built-up index (NDBI), the normalized difference impervious surface index (NDISI), the enhanced normalized differences impervious surface index (ENDISI), and the normalized impervious surface index (NISI). LST and all spectral indices were derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. The seven bioclimatic variables (e.g., air temperature, precipitation) were sourced from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4. We created temporally-aggregated Daymet raster images by calculating mean pixel-values for each season and year, as well as seasonally and annually summed precipitation. We summarized the values of each environmental variable by generating variously-sized (100-m, 500-m, 1000-m) buffers around each bird point count location and extracting weighted mean values of each environmental variable, with each pixel's values weighted by the proportion of its area falling within the buffer. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of s
Long-term composited Modified Normalized Difference Water Index (MNDWI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
Abstract ======== This data package consists of multiple decades of modified normalized difference water index (MNDWI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). By providing a metric by which to reliably identify bodies of open water, these MNDWI data are intended to facilitate analyses of land-based environmental variables (e.g., urbanization, vegetation, land surface temperature) and can also be used to track long-term and seasonal change in the coarse extent of open water as a land-cover type. MNDWI was derived, following the methods of Xu (2006), from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see \'Methods and Protocols\') and accompanying Javascript code. **Citations:** - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18--27. <https://doi.org/10.1016/j.rse.2017.06.031> - Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. *International Journal of Remote Sensing*, *27*(14), 3025--3033. <https://doi.org/10.1080/01431160600589179>
Long-term climate indices (SPEI and scPDSI) derived from monthly meteorology data collected at USHCN stations in the northern Chihuahuan Desert of the United States, 1911-2021
Drought indices — Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI) —where derived from 9 United States Historical Climate Network (USHCN) stations on the Chihuahuan Desert in North America for this dataset. USHCN is a subset of the NOAA Cooperative Observer Program (COOP) Network, which consists of selected sites based on spatial coverages and completeness of data. Monthly precipitation depths, minimum, maximum and mean temperature were pulled from the dataset. These drought indices were derived using the SPEI package and scPDSI packages in R. Potential evapotranspiration was also calculated in R using the Thornthwaite method. All 9 sites are within the bounds of the Chihuahuan Desert in the state of New Mexico, with a single site (EL PASO) in the state of Texas.
Derived SPEI and vapor pressure deficit for 15 NPP study sites on the Jornada Basin, 2013-ongoing
Standardized Precipitation Evapotranspiration Index (SPEI) and minimum, maximum, and average vapor pressure deficit (VPD) were calculated from meteorological data (temperature, precipitation, and relative humidity) from the 15 net primary production (NPP) study locations on the Jornada Experimental Range (JER) and the Chihuahuan Desert Rangeland Research Center (CDRRC) lands in southern New Mexico, U.S.A.
Daily phenocam image data and derived timeseries for global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains daily data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data included here comes from phenocams installed in two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and this dataset includes color values extracted from shrub and grass regions in these images. Further analyses, including daily values of calculated greenness (green chromatic coordinate), precipitation, and temperature, for all the plots included in the study are in EDI dataset knb-lter-jrn.210574002. This study is ongoing.
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
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.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons.
Seasonal sea ice indices including the timing of ice-edge advance and ice-edge retreat (in year day), the ice season duration (in days) and number of actual ice days (versus open water days) within the ice season, extracted for various PAL LTER sub-regions West of the Antarctic Peninsula and derived from passive microwave satellite data for 1979/80 to 2023/24 ice seasons. The ice season duration is defined as the time elapsed between day of ice-edge advance and day of ice-edge retreat within a given sea ice year, which begins mid-February (mean minimum of summer sea ice extent for the Southern Ocean) and ends the following mid-February. See Stammerjohn et al (2008, JGR) for further details.
Average monthly sea ice coverage for various PAL LTER sub-regions West of the Antarctic Peninsula derived from passive microwave satellite data, 1978 - June 2024.
Monthly sea ice coverage derived from passive microwave satellite measurements and extracted for various PAL LTER subregions. Several different sea-ice metrics are provided including (1) monthly sea-ice extent, sea-ice area, and open-water area (km^2) extracted for the greater WAP region (from the AP to 80W) and for 3 PAL LTER grid regions: the original PAL grid (000-900 lines), the PAL 'DSR' grid (200-600 lines), and the PAL 'new' grid (-200 to 600 lines); and (2) monthly sea-ice concentration (%) extracted for small PAL subregions, including the nominal penguin foraging areas (~200km by ~200km) southwest of King George Island (KGI), Anvers, Avian and Charcot islands, as well as for Marguerite Bay (~140km by ~140km) (inland of the area defined for Avian).
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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.