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34 results for “NSF”
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Propagation Measurements and Analyses at 28GHz on NSF POWDER
<p><strong>IEEE ICC 2023: </strong>Propagation Measurements and Analyses at 28GHz via an Autonomous Beam-Steering Platform</p> <p> </p><blockquote> <p>This paper details the design of an autonomous alignment and tracking platform to mechanically steer directional horn antennas in a sliding correlator channel sounder setup for 28-GHz V2X propagation modeling. A pan-and-tilt subsystem facilitates uninhibited rotational mobility along the yaw and pitch axes, driven by open-loop servo units and orchestrated via inertial motion controllers. A geo-positioning subsystem augmented in accuracy by real-time kinematics enables navigation events to be shared between a transmitter and receiver over an Apache Kafka messaging middleware framework with fault tolerance. Herein, our system demonstrates a 3D geo-positioning accuracy of 17 cm, an average principal axes positioning accuracy of 1.1 degrees, and an average tracking response time of 27.8 ms. Crucially, fully autonomous antenna alignment and tracking facilitates continuous series of measurements, a unique yet critical necessity for millimeter wave channel modeling in vehicular networks. The power-delay profiles, collected along routes spanning urban and suburban neighborhoods on the NSF POWDER testbed, are used in pathloss evaluations involving the 3GPP TR38.901 and ITU M.2135 standards. Empirically, we demonstrate that these models fail to accurately capture the 28-GHz pathloss behavior in urban foliage and suburban radio environments. In addition to RMS direction-spread analyses for angles-of-arrival via the SAGE algorithm, we perform signal decoherence studies wherein we derive exponential characteristics of the spatial autocorrelation coefficient under distance and alignment effects.</p> </blockquote> <p></p> <p><strong>Note</strong>: <em>This is a smaller version of our dataset. The original dataset collected on the NSF POWDER testbed is approximately 400 GB. Due to Zenodo's size restrictions, the data uploaded here contains only a few of our calibration (USRP 76 dB gain) and measurement logs (fully-autonomous V2X routes onsite). To gain access to our complete dataset, please contact the authors at <bkeshav1@asu.edu> or <zhan1472@purdue.edu>. Additional measurements in our full dataset include USRP 0 dB calibration results; fully-autonomous urban-stadium-van, urban-campus-cart, and urban-presidents-circle-full-van routes; and semi-autonomous (and manual) urban-garage-cart and urban-campus-cart routes.</em></p>
2018 NSF Large Scale Experiment Workshop on Volcanic Blasts
<p><em><strong>A collection of datasets which were recorded at the 2018 NSF Large Scale Experiment Multiblast workshop on volcanic hazards</strong></em>. The workshop aimed to facilitate interdisciplinary collaboration and improve field-scale testing of monitoring methods and models. The workshop had 47 participants from US-based and international institutions. <a href="https://doi.org/10.1029/2018EO109237">Read some more details in this EOS article</a> or a <a href="https://doi.org/10.31223/X55W4F">full manuscript which is currently in review</a>.</p> <p><strong>attention</strong>: This dataset is UNDER CONSTRUCTION. It is close to, but not absolutely complete. We will publish version 1.0 once the accompanying JGR manuscript has been approved for publication.</p> <p>All data is provided in several zip archives, and small files containing metadata and descriptions. Large data chunks are separated into 'pads' (1–4), which refer to the four experiments that were performed. The archives contain a folder structure, which should allow for compatible extractions, so that archives can be downloaded to a common local folder (e.g. using a script) and extracted there without running into file name conflicts.</p> <p>Several teams collaborated to come up with this dataset. Below we list the teams from which data was used and is part of the current version of the dataset. More data may be published in the future and added in a later version. The teams collaborated to varying degrees for different tasks.</p> <p><strong>Teams</strong> in <em>alphabetical</em> order:</p> <ul> <li>Baylor<br> Baylor University<br> Lead by Kenneth Befus</li> <li>BYU<br> Brigham Young University<br> Lead by Neilsen<br> Contributors: TODO</li> <li>INGV<br> Istituto Nazionale di Geofisica e Vulcanologia, Rome<br> Lead by Taddeucci<br> Contributors: Ricci</li> <li>LDEO<br> Lamont Doherty Earth Observatory, Columbia University<br> Lead by Lev, Oppenheimer</li> <li>MTU<br> Michigan Tech University<br> Lead by Waite<br> Contributors: TODO</li> <li>UB<br> University at Buffalo<br> Lead by Sonder, Valentine<br> Contributors: David Hyman, Kayley DiemKaye, Norman Yu</li> <li>UCSB<br> University of California Santa Barbara<br> Lead by Matoza<br> Contributors: Sean Maher, Richard Sanderson</li> <li>UMKC<br> University of Missouri, Kansas City<br> Lead by Graettinger<br> Contributors: Kadie Bennis</li> <li>Yamagata<br> Yamagata University<br> Lead by Kae Tsunematsu</li> </ul> <p><strong>Parts of This Dataset</strong></p> <ul> <li><em>Coordinates & Positions:</em><br> Lead by the UB team.<br> Coordinates and Locations of Blast Charges, Sensors etc.</li> <li> <p><em>Elevation Data of Craters:</em><br> Lead by the UMKC and LDEO teams (Graettinger, Lev).<br> Elevation data were created from photographs taken right after charge detonations. The 3D-data was derived in a standard photogrammetry software (Metashape™). This data was then rasterized and imported into ArcGIS™, and is provided here. Fine adjustments were made to better match reference locations of the available site coordinate system.</p> </li> <li> <p><em>Ejecta Data:</em><br> Lead by the UMKC team.<br> Spatial distribution of ejected material.<br> The <code>.csv</code> files contain the same information as the Excel sheet, but do not contain any graphs.</p> </li> <li> <p><em>Airborne Pressure Data:</em><br> Lead by the BYU team.<br> Archive files: <code>buy_pad[i].zip</code>.<br> Data is arranged in four zip-archives, one for each blast sequence ("Pad"). Each file contains time and pressure arrays and some metadata of one microphone channel. Individual sensor locations are in the <code>positions.zip</code>.</p> <ul> <li>Format: Matlab <code>.mat</code></li> <li>File name patterns after unpacking:<br> <code>data/BYU Acoustics/Data/Aligned with Infra peaks/Pad [i]/TimeSyncPad[i]Ch[k].mat</code><br> <code>[i]</code>: Pad number (1 ... 4)<br> <code>[k]</code>: Channel number.</li> </ul> </li> <li> <p><em>Seismo-Acoustic Data:</em><br> Lead by two teams, UCSB and MTU, who deployed horizontally distributed (UCSB) and vertically distributed (MTU) seismometer stations, and infrasound sensors. MTU also provided a geophone chain. Some of the UCSB sensors were combined with the rapid BYU provided microphones to record a very wide frequency spectrum in ground and atmosphere.</p> <ul> <li> <p>UCSB data structure:<br> Data archives are provided in sensor groups for all experiments (pads). Archive file names are <code>ucsb_[sensor_type]_[sensor_gid].zip</code>. <code>[sensor_type]</code> is one of <code>inf</code> or <code>seis</code>. <code>[sensor_gid]</code> is an identifier for the sensor group (may also be a single sensor) the archive contains. E.g. <code>inf_nyi1</code> contains infrasound data of sensors <code>NYI1.1</code>, <code>NYI1.2</code> and <code>NYI1.3</code>. Use the preview window above to look into the archives. The <code>position</code> archive contains the sensor locations.</p> </li> <li> <p>MTU data structure:<br> Data archives (<code>mtu_seis-infr_pad[i].zip</code>) are organized in 'pads' 1 ... 4 and contain all sensors (infrasound, seismometer, geophones).</p> </li> </ul> </li> <li> <p><em>Video Material:</em><br> No leading team here. Cameras were contributed teams by INGV, LDEO, Yamagata, UB.<br> A drone was deployed for a map-view. Six or more cameras for each pad. Read the <code>video_readme.pdf</code> for details about camera locations and types.</p> </li> </ul> <p><strong>Changes</strong></p> <ul> <li>v0.5:<br> Added the <code>drone2</code> videos from Baylor. (All video zips were updated!)</li> <li>v0.4:<br> Added analysis pack 1 (<code>multiblast_analysis-pack1.zip</code>) code that produced figures and tables of the ms in review. This code is also available on <a href="https://gitlab.com/isonder/2018_blasts">gitlab.com/isonder/2018_blasts</a>.</li> <li>v0.3:<br> Added <code>mtu_seis_infr_metadata.zip</code>. Metadata for the MTU dataset.</li> <li>v0.2:<br> Second batch of main data. >90% complete, I guess.</li> <li>v0.1:<br> First batch of main data.</li> </ul>
2021 NSF Research Master File
Open the record for dataset details and reuse information.
2022 NSF Research Master File
Open the record for dataset details and reuse information.
2024 NSF Research Master File
Open the record for dataset details and reuse information.
2023 NSF Research Master File
Open the record for dataset details and reuse information.
Bacterial production and respiration data set for NSF Arctic Photochemistry project on the North Slope of Alaska.
Data file describing the bacterial production and bacterial respiration of water samples collected at various sites near Toolik Lake on the North Slope of Alaska. Sample site descriptors include site, date, time, depth, and category representing severity of thermokarst disturbance. A synthesis of the data presented here is published in Cory et al. 2013, PNAS 110:3429-3434, and in Cory et al. 2014, Science 345:925-928.
Biogeochemistry data set for NSF Arctic Photochemistry project on the North Slope of Alaska.
Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake on the North Slope of Alaska. Sample site descriptors include a unique assigned number (sortchem), site, date, time, depth, and category (level of thermokarst disturbance). Physical measures collected in the field include temperature, electrical conductivity, and pH. Chemical analyses include alkalinity; dissolved organic carbon (DOC); inorganic and total dissolved nutrients (NH4, PO4, NO3, TDN, TDP); particulate carbon, nitrogen, and phosphorus (PC, PN, and PP); cations (Ca, Mg, Na, K); anions (Cl, SO4); and silica. A synthesis of much of the data presented here is published in Cory et al. 2013, PNAS 110:3429-3434; Cory et al. 2014, Science 345:925-928; and Page et al. 2013, Environment, Science, &amp;amp; Technology 47:12860−12867.
Light profile data set for NSF Photochemistry project on the North Slope of Alaska.
Data file containing the irradiance profile with depth in two rivers on the North Slope of Alaska near Toolik Lake . Variables include site, depth, and wavelength. A synthesis of the data presented here is published in Cory et al. 2013, PNAS 110:3429-3434, and in Cory et al. 2014, Science 345:925-928.
Apparent quantum yield data set for NSF Photochemistry project on the North Slope of Alaska.
Data file describing the apparent quantum yield of photo-oxidation, photo-mineralization, and photo-stimulated microbial respiration of dissolved organic carbon in water samples collected at various sites near Toolik Lake on the North Slope of Alaska. A synthesis of the data presented here is published in Cory et al. 2013, PNAS 110:3429-3434, and in Cory et al. 2014, Science 345:925-928.
Photochemistry data set for NSF Photochemistry project on the North Slope of Alaska.
Data file containing optical characterization of colored dissolved organic matter (CDOM). Data include CDOM absorption coefficients, water column light attenuation coefficients, specific UV light absorbance (SUVA254), spectral slope ratio, and fluorescence index from waters near Toolik Lake on the North Slope of Alaska. A synthesis of the data presented here is published in Cory et al. 2013, PNAS 110:3429-3434, and in Cory et al. 2014, Science 345:925-928.
QC-ed Anemometer data from NSF NCAR Mesa Lab
<p>QC-ed datasets compared to <a href="https://doi.org/10.5281/zenodo.14060801" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14060801</a></p> <p>WXT instrument Sep 24,1996 through March 25, 2024, 5 minute data, <strong>quality controlled</strong>, netcdf, from EOL , NSF NCAR</p> <p>Gill instrument Nov 29, 2021- March 25, 2024, 1 minute data, <strong>quality controlled</strong> netcdf, from EOL, NSF NCAR</p> <p>BAMS: Earth, wind and fire: Are Boulder’s hurricane-force downslope winds changing? Authors: Gerald A. Meehl*, Christine A. Shields, Brendan M. Myers, McKenzie L. Larson, Dale Durran, Muntaha Pasha, Annareli Morales, Aneesh Subramanian, Andrew C. Winters, Paul Schlatter, and Morris Weisman</p>
Anemometer data from NSF NCAR Mesa Lab
<p>WXT instrument Sep 24,1996 through March 25, 2024, 5 minute data, unfiltered data, netcdf, from EOL , NSF NCAR</p> <p>Gill instrument Nov 29, 2021- March 25, 2024, 1 minute data, unfiltered data, netcdf, from EOL, NSF NCAR</p> <p>BAMS: Earth, wind and fire: Are Boulder’s hurricane-force downslope winds changing? Authors: Gerald A. Meehl*, Christine A. Shields, Brendan M. Myers, McKenzie L. Larson, Dale Durran, Muntaha Pasha, Annareli Morales, Aneesh Subramanian, Andrew C. Winters, Paul Schlatter, and Morris Weisman</p>
Nephrogenic Systemic Fibrosis (NSF): Analysis of Tissue Gadolinium Levels
ClinicalTrials.gov study NCT01014754. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Single-molecule two- and three-colour FRET studies reveal a hidden transition state in SNARE disassembly by NSF
<p>Raw Data for the NC manuscript</p>
Observational Study on the Incidence of NSF in Renal Impaired Patients Following Dotarem Administration
ClinicalTrials.gov study NCT01467271. IPD Sharing: Not stated. Countries: 11. Publications: 0.
Risk of Nephrogenic Systemic Fibrosis (NSF) in Patients With Moderate Renal Insufficiency After the Administration of Magnevist
ClinicalTrials.gov study NCT00744939. IPD Sharing: Not stated. Countries: 1. Publications: 0.
DC3 Miscellaneous NSF/NCAR GV-HIAPER Data
DC3_Miscellaneous_NSF-GV-HIAPER_Data are miscellaneous data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. This product features data from the Global Forecast System (GFS) model. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
DC3 In-Situ NSF/NCAR GV-HIAPER Cloud Data
DC3_Cloud_AircraftInSitu_NSF-GV-HIAPER_Data are in-situ cloud data collected onboard the NSF/NCAR GV-HIAPER aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete.The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection.DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry.In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere.
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.