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1,855 results for “Autonomous”
PARP Inhibition Exacerbates Tumor-autonomous Immunogenicity Driven by BRCA1 Deficiency through DNA Sensing and Interferon Response in Ovarian Cancer
GEO Series GSE120792. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Non-cell Autonomous Regionalization of Sertoli Valve Epithelia in the Terminal Segment of the Seminiferous Tubules of Mouse Testes
GEO Series GSE112015. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomics uncovers a non-autonomous Tbx1-dependent genetic program controlling cardiac neural crest cell development
GEO Series GSE210521. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Autonomous transposons tune their sequences to ensure somatic suppression [polyA_HEK293]
GEO Series GSE223257. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
The Autonomous Hepatocyte Clock Controls Acetaminophen Bioactivation and Chronotoxicity [Liver]
GEO Series GSE34442. Mus musculus. 24 samples. Type: Expression profiling by array.
Partial in vivo reprogramming enables injury-free intestinal regeneration via autonomous Ptgs1 induction [RNAseq_Fetal]
GEO Series GSE238056. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
AKT Inhibition Promotes Non-autonomous Cancer Cell Survival
GEO Series GSE71901. Homo sapiens. 40 samples. Type: Protein profiling by protein array; Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
Molecular organization of autonomic, respiratory, and spinally-projecting neurons in the mouse ventrolateral medulla
GEO Series GSE272976. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Imprinted Cdkn1c Genomic Locus Cell-Autonomously Promotes Cell Survival in Cerebral Cortex Development [Cdkn1c_allelic expression]
GEO Series GSE138229. Mus musculus. 5 samples. Type: Other.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) GCPEX V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) GCPEx dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The GPM Cold-season Precipitation Experiment (GCPEx) addressed shortcomings in the GPM snowfall retrieval algorithm by collecting microphysical properties, associated remote sensing observations, and coordinated model simulations of precipitating snow. These data sets were collected to aid in the achievement of the over arching goal of GCPEx which is to characterize the ability of multi-frequency active and passive microwave sensors to detect and estimate falling snow. The APU dataset for GCPEx provides precipitation data including raindrop size, raindrop counts, precipitation drop size, precipitation rate, precipitation amount, and snowflake size, counts and distribution. The GCPEx APU data was collected from several sites in Canada during the Winter 2011-2012 period.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) NSSTC V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) NSSTC dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer based on single particle extinction that measures particle size and fall velocity. This APU consists of the Parsivel, which was developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. This dataset provides rainfall data for the Global Precipitation Measurement (GPM) Mission Ground Validation Experiment collected at the National Space Science Technology Center (NSSTC) in Huntsville, Alabama. The validation effort will entail numerous GPM-specific and joint-agency/international external field campaigns, using state of the art cloud and precipitation observational infrastructure. Surface rainfall will be measured by very dense rain gauge and disdrometer networks at various field campaign sites. There may be occasional gaps in the data when the instrument is not resident at the NSSTC and is sent to participate in field campaigns.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) LPVEX V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) LPVEx dataset provides rainfall data for the Global Precipitation Measurement (GPM) Misson Ground Validation Experiment collected at four sites in Finland: Harmaja, Emasalo, Jarvenpaa, and the Gulf of Finland (Aranda) during the Light Precipitation Validation Experiment (LPVEx), which took place during September and October of 2010. The experiment leveraged in situ microphysical property measurements, coordinated remote sensing observations, and cloud resolving model simulations of high latitude precipitation systems to conduct a comprehensive evaluation of precipitation algorithms for current and future satellite platforms.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) HYMEX V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) HyMeX dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The HYdrological cycle in Mediterranean EXperiment (HyMeX) aimed to improve the understanding, quantification and modelling of the hydrological cycle in the Mediterranean, with emphasis on the predictability and evolution of extreme weather events, inter-annual to decadal variability of the Mediterranean coupled system, and associated trends in the context of global changeThe APU dataset for HyMeX provides precipitation data including raindrop size, raindrop counts, precipitation drop size, precipitation rate, precipitation amount, and snowflake size, snowflake counts, and snowflake distribution. The HyMeX APU data were collected in Italy and France from September to November 2012.
Autonomous Ocean Sampling Networks (AOSN) second deployment
Measurements made under the Autonomous Ocean Sampling Networks (AOSN) second deployment in the Monterey Bay area in 2003.
Autonomous Radiometric Validation of PACE OCI in the Gulf of Mexico
Validate PACE OCI remote sensing reflectance using uncrewed autonomous vessel measuring water-leaving radiance with two methods along with inherence optical properties in the Gulf of Mexico.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) MC3E V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) MC3E dataset was collected by the Autonomous Parsivel Unit (APU), which is an optical disdrometer that measures the size and fall velocity of single precipitation particles. The APU consists of the Parsivel (the precipitation measuring instrument), developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville. The APU dataset for the Midlatitude Continental Convective Clouds Experiment (MC3E) provides precipitation data including raindrop size, precipitation drop size, precipitation rate and amount. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place in central Oklahoma during the April-June 2011 period. The experiment was a collaborative effort between the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility and the National Aeronautics and Space Administration's (NASA) Global Precipitation Measurement (GPM) mission Ground Validation (GV) program. The field campaign leveraged the unprecedented observing infrastructure currently available in the central United States, combined with an extensive sounding array, remote sensing and in situ aircraft observations, NASA GPM ground validation remote sensors, and new ARM instrumentation purchased with American Recovery and Reinvestment Act funding. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms.
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) IPHEx V1
The GPM Ground Validation NASA Autonomous Parsivel Unit (APU) IPHEx dataset was acquired by multiple parsivel instruments during the GPM Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in western North Carolina. IPHEx sought to characterize warm season orographic precipitation regimes, and the relationship between precipitation regimes and hydrologic processes in regions of complex terrain. The APU, an optical disdrometer based on single particle extinction, measures particle size, and fall velocity. ASCII encoded data files contain information on the drop size distribution and integral precipitation parameters such as precipitation rate, reflectivity, and mass-weighted mean diameter.
PISTON 2018-2019 Autonomous Platform Ocean Datasets
PISTON-ONR-NOAA-Autonomous-Uncrewed_2018-2019 is the Propagation of Intra-Seasonal Tropical Oscillations (PISTON) 2018-2019 autonomous platform ocean data product. This product is the result of a joint effort that involved NASA as well as the Office of Naval Research (ONR), and National Oceanic and Atmospheric Administration (NOAA). Data was collected collection for this product using Sounding Oceanographic Lagrangian Observer II (SOLO-II) instruments. Data collection is complete.The PISTON field campaign, sponsored by the Office of Naval Research (ONR) and the National Oceanic and Atmospheric Administration (NOAA), was designed to gain understanding and enhance the prediction capability of multi-scale tropical atmospheric convection and air-sea interaction in this region. PISTON targeted the Boreal Summer Intraseasonal Oscillation (BSISO), which defines the northward and eastward movement of convection associated with equatorial waves, the MJO, tropical cyclones, and the Maritime Continent monsoon during northern-hemispheric (boreal) summertime. PISTON completed three total shipboard cruises, deployed eight drifting ocean profiling floats and two full-depth ocean moorings, collaborated with a Japanese research vessel collecting similar data, and also made use of soundings from nearby islands. These activities took place in the Philippine Sea, which is in the tropical northwestern Pacific Ocean north of Palau, between August 2018 - September 2019, with each dataset spanning a slightly different amount of time. There were two US research vessels involved in PISTON: R/V Thomas G. Thompson in Aug-Sept and Sept-Oct 2018 and R/V Sally Ride in Sept 2019. The first 2018 cruise coincided collaborative activities with R/V Mirai. The 2019 cruise coincided with the NASA CAMP2Ex airborne field experiment (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, please see more info below). The two specialized moorings were deployed north of Palau and collected data from August 2018 - Oct 2019 to document a time series of ocean characteristics beneath typhoons and other tropical weather disturbances. Toward the same goal, eight profiling ocean floats were also deployed ahead of typhoons in 2018. For characterization of clouds and precipitation, the PISTON shipboard instrument payload included a scanning C-band dual-polarization Doppler radar (SEA-POL), a vertically-pointing Doppler W-band radar, and multiple vertically- and horizontally-scanning lidars. Rawinsondes were launched from the ships for atmospheric profiling. Additional radiosonde and precipitation radar data were collected from R/V Mirai via an international collaboration. Regular soundings were also archived from islands neighboring the Philippines and the Philippine Sea: Dongsha Island, Taiping Island, Yap, Palau, and Guam. Additional atmospheric sampling from the PISTON R/V Thompson 2018 and Sally Ride 2019 cruises included an electric field meter and disdrometer in 2018, and all-sky camera images in 2019. To document near-surface meteorological conditions, air-sea fluxes, and upper-ocean variability including ocean vertical profiles on these cruises, instruments were deployed on and towed from the ship. Additional profiles of ocean acoustics and oceanic chemistry were not archived but are available upon request by James N. Moum, Oregon State University, jim.moum@oregonstate.edu. A forecast team analyzed and predicted conditions of the weather and ocean throughout the PISTON experiment, which were not archived but are available upon request for future modeling and observational analysis studies (contacts: Sue Chen, US Naval Research Lab Monterey, sue.chen@nrlmry.navy.mil and Michael M. Bell, Colorado State University, mmbell@colostate.edu). There are five total DOIs related to PISTON, separated by ship (and therefore year) as well as other platforms/locations that span multiple years:https://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVTHOMPSON/DATA001 https://doi.org/10.5067/SUBORBITAL/PISTON2019-ONR-NOAA/RVSALLYRIDE/DATA001https://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/AUTONOMOUS/DATA001 (this doi)https://doi.org/10.5067/SUBORBITAL/PISTON2018-2019-ONR-NOAA/ISLANDS/DATA001https://doi.org/10.5067/SUBORBITAL/PISTON2018-ONR-NOAA/RVMIRAI/DATA001The CAMP2Ex 2019 data DOI is:https://doi.org/DOI: 10.5067/Suborbital/CAMP2EX2018/DATA001The CAMP2Ex (Clouds, Aerosol and Monsoon Processes-Philippines Experiment, 2019) and PISTON (Propagation of Intra-Seasonal Tropical Oscillations, 2018-2019) were two field studies conducted collaboratively in the Southeast Asian region. While each study had its own set of science objectives, there were common and complementary science goals and instrument payloads between these two projects. Consequently, a synergistic partnership was established at the very beginning of the projects and a coordinated sampling strategy was developed to extend spatial coverage and obtain temporal context information, which bene
GPM GROUND VALIDATION AUTONOMOUS PARSIVEL UNIT (APU) IFLOODS V1
The GPM Ground Validation Autonomous Parsivel Unit (APU) IFloodS dataset collected data from several sites in eastern Iowa during the spring of 2013. The APU dataset for the Iowa Flood Studies (IFloodS) Field Experiment provides precipitation data including precipitation drop size, counts, and distribution. The goals of the campaign were to collect detailed measurements of precipitation at the Earth's surface using ground instruments and advanced weather radars and, simultaneously, collect data from satellites passing overhead. The APU is an optical disdrometer based on single particle extinction that measures particle size and fall velocity. This APU consists of the Parsivel, which was developed by OTT in Germany, and its support systems, which were designed and built by the University of Alabama in Huntsville.
Autonomous Deep-Space Optical Navigation Project
<p>Every one of the future exploration architectures being considered by NASA have, at their core, the need to rendezvous and dock with other vehicles or bodies.&nbsp; Future manned vehicles need to be able to do so with both cooperative and uncooperative vehicles and objects.&nbsp; To this end, the sensors being considered are all optical-based.&nbsp; In fact, passive sensors, such as IR cameras and visual cameras, are at the heart of any exploration architecture.&nbsp; There is a need for the onboard systems to be able to use the images provided by these sensors to rendezvous and dock/capture these objects.&nbsp; Therefore, this project will develop this capability to operate around a variety of objects, without a priori knowledge of their geometry.&nbsp; In particular, a technology called &lsquo;optical flow&rsquo; or &lsquo;visual odometry&rsquo; (VO), will be harnessed to develop a robust on-board capability using passive sensors; of course, if active sensors are available, they will be used as well. In fact, we will also apply this technique to navigating around a cratered object (such as an asteroid). This project will enhance the Agency&rsquo;s ability to operate at distant locations, without the need for ground intervention.</p><p>To date, all of the on-board navigation development performed has focused on either Low Earth Orbit (LEO) or Low Lunar Orbit (LLO).&nbsp; We seek to advance deep-space navigation technology by focusing this Internal Research and Development (IRAD) upon rendezvous and navigation in a weak gravity environment, either at Lagrangian point 2 (L2) or around an asteroid.&nbsp; Of course, this will apply to any destinations that have a strong gravity field as well.&nbsp; As well, the technology developed in this Internal Research and Development will apply to rendezvousing with vehicles such as ISS.&nbsp; We choose to focus our IRAD effort on the navigation algorithms and software for the ARCM DRO Mission, thus broadening our scope, maintaining our cutting-edge capability, and advancing US manned space exploration.&nbsp; The goal is to be flexible enough to meet the needs of the NASA vision, as it applies to any destination the Agency chooses to embark upon.</p><p>&nbsp;</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.