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42 results for “2016-2017”
An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times
<p>Catalog of 440,697 earthquakes of the 2016-2017 Central Italy seismic sequence semi-automatically generated by Spallarossa et al. (2020). The catalogue covers one year of aftershocks following the first mainshock of the sequence (from 08242016 to 08312017).</p> <p>The catalog has been generated using the Complete Automatic Seismic Processor (CASP) procedure (Scafidi et al., 2019) to detect the events and an advanced picker engine (RSNI-Picker<sub>2</sub>; Scafidi et al., 2018; Spallarossa et al., 2014) to determine their phase arrival times. The final set of about 7 million P- and 10 million S-wave arrival times have been used to locate the events using a non-linear location algorithm (NonLinLoc; Lomax et al. 2000), with a 1D velocity model calibrated for the area (De Luca et al., 2009) and station corrections. For each event, also local magnitudes (M<sub>L</sub>) has been calculated as well as a locations quality.</p> <p>Earthquake locations quality has been classified by means of the procedure proposed by Michele et al., (2019) consisting of the combination of diverse uncertainty parameters provided by the NonLinLoc location code. Locations quality is provided in terms of a unique numeric normalized value, named quality factor, varying between qf=0 (best quality location) and qf=1 (worst quality location). Then locations have been assigned to a quality class depending on the qf parameter value according to the following scheme: A-class (0 < qf ≤ 0.25), B-class (0.25 < qf ≤ 0.50), C-class (0.50 < qf ≤ 0.75), and D-class (0.75 < qf < 1.00). The earthquake locations are distributed between the quality classes as A-30.6%, B-31.4%, C-18.6%, and D-19.4% (details in Spallarossa et al., 2020).</p> <p>We accompanied the catalogue with the 30 events with M>3.5 missed by our procedure (bring the total number of events to 440,727), including the first Amatrice mainshock (M<sub>W</sub>6.0; see Spallarossa et al., 2020). These 30 missing events recognisable by the ID starting with ISI), have been taken from INGV bulletin (<a href="http://terremoti.ingv.it">http://terremoti.ingv.it</a>; ISIDe Working Group., 2007), manually generated. These additional events report INGV locations and magnitude parameters while are missing related quality factors and quality class, being generated by a different procedure.</p> <p>We added to the larger events, the available moment magnitudes (M<sub>W</sub>) from Time Domain Moment Tensor catalogue (<a href="http://terremoti.ingv.it/tdmt">http://terremoti.ingv.it/tdmt</a>; Scognamiglio et al., 2006).</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-event – ID</li> <li>Latitude (°) expressed in decimal degrees - LAT</li> <li>Longitude (°) expressed in decimal degrees - LON</li> <li>Depth(km) hypocentral depth expressed in kilometres - DEP</li> <li>Year of origin time in the format yyyy - YR</li> <li>Month of origin time in the format mo - MON</li> <li>Day of origin time in the format dd - DY</li> <li>Hour of origin time in the format hh - HR</li> <li>Minute of origin time in the format mi - MIN</li> <li>Second of origin time in the format XX.XXX s - SEC</li> <li>Local Magnitude - ML</li> <li>Standard deviation of the Local Magnitude – STD</li> <li>Moment Magnitude – Mw (from TDMT)</li> <li>Horizontal Error (from NLL output) (km) expressed in kilometres - ERH</li> <li>Vertical Error (from NLL output) (km) expressed in kilometres - ERZ</li> <li>RMS (from NLL output) (s) expressed in seconds - RMS</li> <li>Number of Phases – NPHS</li> <li>Stations Azimuthal GAP (°) expressed in decimal degrees - GAP</li> <li>Quality factor - Qf</li> <li>Quality class - Qc</li> </ul> <p> </p> <p>De Luca G., M. Cattaneo, G. Monachesi and A, Amato (2009). Seismicity in the Umbria-Marche region from the integration of national and regional seismic networks. Tectonophysics, 476(1), 219-231. doi: 10.1016/j.tecto.2008.11.032.</p> <p>ISIDe Working Group. (2007). Italian Seismological Instrumental and Parametric Database (ISIDe). Istituto Nazionale di Geofisica e Vulcanologia (INGV); https://doi.org/10.13127/ISIDE.</p> <p>Lomax, A., J. Virieux, P. Volant, and C. Berge-Thierry (2000). Probabilistic earthquake location in 3D and layered models: introduction of a Metropolis–Gibbs method and comparison with linear locations. In: Advances in seismic event location, ed. C. H. Thurber and N. Rabinowitz, 101–134. Dordrecht and Boston: Kluwer Academic Publishers.</p> <p>Michele, M., Latorre, D., Emolo, A. (2019). An Empirical Formula to Classify the Quality of Earthquake Locations. Bulletin of the Seismological Society of America. Vol. 109, No. 6, pp. 2755–2761, December 2019, doi: 10.1785/0120190144.</p> <p>Scafidi, D., Viganò A., Ferretti G., and Spallarossa D. (2018). Robust picking and accurate location with RSNI-Picker2: real-time automatic monitoring of earthquakes and non-tectonic events, Seismol. Res. Lett, Vol. 89 (4), pp. 1478-1487, doi: 10.1785/0220170206.</p> <p>Scafidi D, Spallarossa D, Ferretti G, Barani S, Castello B, Margheriti L (2019). A complete automatic procedure to compile reliable seismic catalogs and travel-time and strong-motion parameters datasets. Seismol Res Lett 90(3):1308–1317.</p> <p>Scognamiglio, L., Tinti, E., Quintiliani, M. (2006). Time Domain Moment Tensor [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/TDMT.</p> <p>Spallarossa, D., G. Ferretti, D. Scafidi, C. Turino, and M. Pasta (2014). Performance of the RSNI-Picker, Seismol. Res. Lett. 85, 1243–1254.</p> <p>Spallarossa D., Cattaneo M., Scafidi D., Michele M., Chiaraluce L., Segou M. and I. G. Main (2020). An automatically generated high-resolution earthquake catalogue for the 2016-2017 Central Italy seismic sequence, including P and S phase arrival times. Geophys. J. Int. doi: 10.1093/gji/ggaa604.</p>
Time-lapse observations of snow bedforms in the Colorado Front Range, 2016-2017
<p><strong>Description:</strong> Time-lapse imagery of snow surfaces on Niwot Ridge, in the Colorado Front Range, between March 2016 and April 2017.</p> <p><strong>Purpose:</strong> Videos show the evolution of snow bedforms, including the formation and movement of snow dunes, snow ripples, and sastrugi. Used to analyze the modes of bedform movement, and to constrain the weather conditions in which they form.</p> <p><strong>Equipment, details + weather data: </strong>Time-lapse cameras are Day6 Plotwatcher Pros. They take images every 10s during daylight hours. The cameras are located at -40.054307, -105.590764, 150m upwind of the Niwot Ridge LTER Saddle Station. Bedform movement can thus be correlated with climate and precipitation data from the station, provided at niwot.colorado.edu</p> <p><strong>Filenames:</strong> Files are named yymmddAA.avi, where yy/mm/dd is the date of the recording. When multiple recordings were made in one day, there will be a yymmddAB.avi, yymmddAC.avi, etc. Some filenames are annotated with a description, eg 170226AA-sastrugi.avi. On a few dates (November 2016) the cameras recorded incorrect dates; on these days, the dates in the filenames are to be preferred.</p> <p>Date and time in video: the date, time and temperature are recorded in the bottom of the videos. The date and time are generally accurate, but if they differ from the date in the filename, that date is to be preferred and the time disregarded. Temperature in the film is generally colder than temperature recorded by the nearby weather station, and we generally disregarded it in favor of climate data from niwot.colorado.edu.</p> <p><strong>Data format:</strong> Data are presented as .avi files, which may be run by most video players. Data were originally recorded with proprietary .tlv file extension. The .tlv format is equivalent to .avi; if any .tlv files are found, simply rename them as .avi files and play as usual.</p> <p><strong>Acknowledgements:</strong> This research was supported by a Department of Energy Computational Science Graduate Fellowship (DE-FG02-97ER25308), by a University of Colorado Chancellor's Fellowship, and by the National Science Foundation via support for the Boulder Creek Critical Zone Observatory (EAR-1331828).<br> Field equipment was funded by the American Alpine Club; the Colorado Scientific Society; and a Patterson Award from the University of Colorado Department of Geological Sciences. Field assistants were funded by the University of Colorado Undergraduate Research Opportunities Program. Logistical support and climate data were provided by the NSF-supported Niwot Ridge Long-Term Ecological Research Project and the University of Colorado Mountain Research station.</p>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
MicroED data collected from ribbon-shaped (originally thought to be needle-shaped) orthorhombic lysozyme crystals (2016-2017)
<p>Raw data used in the following journal article:</p> <p>Xu et al., 2018, Structure26, 667–675</p> <p>https://doi.org/10.1016/j.str.2018.02.015</p> <p>Information of the data is included in the ReadME.pdf</p>
Socioeconomic Inequalities in the Diagnosis and Treatment of Colon and Ovarian Cancer in England Between 2016-2017
ClinicalTrials.gov study NCT05185388. IPD Sharing: NO. Countries: 1. Publications: 1.
Safety and Immunogenicity of Fluzone® Quadrivalent and Fluzone® High-Dose, Influenza Vaccines, 2016-2017 Formulations
ClinicalTrials.gov study NCT02908269. IPD Sharing: YES. Countries: 1. Publications: 0.
SnowMicroPen Measurements on Sea Ice 2016-2017
<p>Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) penetrometer measurements. The SMP profiles provide detailed information about the snow microstructure and stratigraphy and were collected to evaluate coincident airborne and satellite measurements aimed at improving the understanding of inter-annual variability of Arctic snow and sea ice properties. The measurements were acquired during two April field campaigns conducted near the time of maximum snow thickness, coinciding with NASA Operation IceBridge (17 April 2016) and ESA CryoVEx (26 April 2017). In total, 8 survey sites were evaluated within the Canadian Arctic Archipelago near Eureka (80.0°N 85.9°W) and 6 survey sites on the Arctic Ocean (spanning 83.4°N and 86.3°N), on both first (FYI) and multi-year (MYI) sea ice. The SMP measurements were recorded using a SnowMicroPen® 4 penetrometer.</p>
(Ceríaco et al. 2016a:27); "Espinheira" [-16.78886, 12.35761] (Ceríaco et al. 2016a:28). Taxonomic and distributional notes: Rhoptropus biporosus is closely allied to R. barnardi and one or more undescribed species of small-bodied congeners that occupy similar habitats in arid to semi-arid rocky areas bordering the Namib (Kuhn 2016). MAP 150. Distribution of Rhoptropus biporosus in Angola. in Diversity and Distribution of the Amphibians and Terrestrial Reptiles of Angola Atlas of Historical and Bibliographic Records (1840-2017)
(Ceríaco et al. 2016a:27); "Espinheira" [-16.78886, 12.35761] (Ceríaco et al. 2016a:28). Taxonomic and distributional notes: Rhoptropus biporosus is closely allied to R. barnardi and one or more undescribed species of small-bodied congeners that occupy similar habitats in arid to semi-arid rocky areas bordering the Namib (Kuhn 2016). MAP 150. Distribution of Rhoptropus biporosus in Angola.
FluView National, Regional, and State Level Outpatient Illness and Viral Surveillance 2016-2017 (archived by MIDAS-ISG)
<p>Description from the FluView Interactive web application (from which these files were downloaded):</p> <p>Viral Surveillance — Data collection from both the U.S. World Health Organization (WHO) Collaborating Laboratories and National Respiratory and Enteric Virus Surveillance System (NREVSS) laboratories began during the 1997-98 season. The volume of tested specimens has greatly increased during this time due to increased participation and increased testing. During the 1997-98 season 43 state public health laboratories participated in surveillance, and by the 2004-05 season all state public health laboratories were participating in surveillance. The addition of NREVSS data during the 1997-98 season roughly doubled the amount of virologic data reported each week. </p> <p>The number of specimens tested and % positive rate vary by region and season based on different testing practices including triaging of specimens by the reporting labs, therefore it is not appropriate to compare the magnitude of positivity rates or the number of positive specimens between regions or seasons. </p> <p>The U.S. WHO and NREVSS collaborating laboratories report the total number of respiratory specimens tested and the number positive for influenza types A and B each week to CDC. Most of the U.S. WHO collaborating laboratories also report the influenza A subtype (H1 or H3) of the viruses they have isolated, but the majority of NREVSS laboratories do not report the influenza A subtype. </p> <p>For more information on virologic surveillance please visit:http://www.cdc.gov/flu/weekly/overview.htm#Viral</p> <p>Outpatient Illness Surveillance — Information on patient visits to health care providers for influenza-like illness is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). This collaborative effort between CDC, state and local health departments, and health care providers started during the 1997-98 influenza season when approximately 250 providers were enrolled. Enrollment in the system has increased over time and there were >3,000 providers enrolled during the 2010-11 season.</p> <p>The number and percent of patients presenting with ILI each week will vary by region and season due to many factors, including having different provider type mixes (children present with higher rates of ILI than adults, and therefore regions with a higher percentage of pediatric practices will have higher numbers of cases). Therefore it is not appropriate to compare the magnitude of the percent of visits due to ILI between regions and seasons.</p> <p>Baseline levels are calculated both nationally and for each region. Percentages at or above the baseline level are considered to be elevated.</p> <p>For more information on ILI surveillance and baselines please visit:http://www.cdc.gov/flu/weekly/overview.htm#Outpatient</p>
Study to Evaluate the Safety of 1 New 6:2 Influenza Virus Reassortant in Adults for the 2016-2017 Season
ClinicalTrials.gov study NCT02743117. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ATom: Observed and Modeled Organic Aerosol Mass Concentrations, 2016-2017
This dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aerosol Microphysical Properties package (AMP), black carbon mass content measured by the Single Particle Soot Photometer (NOAA SP2), and refractory and non-refractory aerosol composition measured by the Particle Analysis By Laser Mass Spectrometry (PALMS). Both observed and modeled data are provided at a 60-second temporal resolution. The data are provided in netCDF format.
FLEXPART Influence Functions for ACT-America, 2016-2017
This dataset contains a set of Lagrangian particle dispersion simulations of carbon dioxide concentrations using the FLEXible PARTicle (FLEXPART) model. FLEXPART quantified the source-receptor relationships, so-called "influence functions", in a backward mode. The simulations were constructed for five Atmospheric Carbon and Transport America (ACT-America) deployments over the eastern U.S. that occurred in 2016-2019. Each receptor of the influence function is the 30-second or 10-minute interval along flight tracks, characterized by a box with boundaries between the maximum and minimum latitude/longitude as well as between the maximum and minimum altitudes during the interval. Each receptor box released 5,000 particles and simulated their transport and dispersion backward for 10 or 20 days. The simulations were driven by 27-km meteorology provided by the WRF-Chem simulation or by ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Background levels of carbon dioxide were obtained from CarbonTracker and OCO-2 v9 MIP. The data are provided in netCDF and FLEXPART binary formats.
Soil Respiration Maps for the ABoVE Domain, 2016-2017
This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.
Methane Plumes Derived from AVIRIS-NG over Point Sources across California, 2016-2017
This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey. The survey was designed to cover at least 60% of the methane point source infrastructure in California guided by the Vista-CA dataset of identified locations of potential methane emitting facilities and infrastructure in three primary sectors (energy, agriculture, and waste). The purpose of the survey was to detect, quantify, and attribute point source emissions to specific infrastructure elements to improve the scientific understanding of regional methane budgets and to inform policy and planning activities that reduce methane emissions.
Carbon Pools across CONUS using the MaxEnt Model, 2005, 2010, 2015, 2016, and 2017
This dataset provides annual estimates of six carbon pools, including forest aboveground live biomass, belowground biomass, aboveground dead biomass, belowground dead biomass, litter, and soil organic matter, across the conterminous United States (CONUS) for 2005, 2010, 2015, 2016, and 2017. Carbon stocks were estimated using a modified MaxEnt model. Measurements of pixel-specific site conditions from remote sensing data were combined with field inventory data from the U.S. Forest Service Forest Inventory and Analysis (FIA). Remote sensing data inputs included Thematic Mapper on Landsat 5, Operational Land Imager on Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua, microwave radar measurements from Phased Array type L-band Synthetic Aperture Radar (PALSAR) on Advanced Land Observation Satellite (ALOS) and PALSAR-2 ALOS-2, airborne imagery from National Agriculture Imagery Program (NAIP), and the digital elevation model from the Shuttle Radar Topography Mission (SRTM). Data from satellite and airborne sources were co-registered on a common 100 m (1 ha) grid.
CARAFE: Regional Airborne Greenhouse Gases Eddy Covariance Measurements, 2016-2017
This dataset provides airborne eddy covariance (EC) fluxes of carbon dioxide, methane, sensible heat, and latent heat at high spatial resolution collected during the NASA Carbon Airborne Flux Experiment (CARAFE) airborne 2016 and 2017 campaigns. CARAFE utilized the NASA C-23 Sherpa aircraft with a suite of commercial and custom instrumentation. Deployment occurred across the Mid-Atlantic Region for the period 2016-09-07 through 2016-09-26 and 2017-05-03 through 2017-05-26. The data also include downwelling radiation, water vapor, pressure, temperature, wind, and aircraft navigation data. Airborne EC can quantify surface fluxes at local to regional scales, potentially helping to bridge gaps between top-down and bottom-up flux estimates and offering novel insights into biophysical and biogeochemical processes.
ATom: DC-8 Forward Camera Videos, 2016-2017
This dataset contains images taken from the front of the NASA DC-8 aircraft during the first three ATom campaigns from 2016-2017. Images were taken with an Axis P1357 High Definition camera with a Theia TH138A wide-angle lens. These images were then stitched together at a 10-second frequency into an MP4 (*.mp4) video for each flight. The forward camera shows the visible atmosphere that DC-8 flew through, allowing the in situ measurements to be placed in the context of cloud fields, smoke and haze layers, and boundary layers.
ABoVE: Environmental Conditions and Subsistence Resource Access, Alaska, 2016-2017
This dataset provides descriptions and photos of environmental conditions that impacted availability to subsistence resources by residents in nine rural communities within the Yukon River basin of Interior Alaska. The data (photos) were collected by citizens (harvesters) residing in the communities while engaged in subsistence harvesting activities. The data include descriptions of the environmental condition captured in the photo, photo date, an explanation of how the condition influenced travel and access to resources, the subsistence activity when the photo was taken, effects of the environmental condition on the participant's safety, and the participant's observations regarding frequency and extent of the condition. A sensitivity metric was derived that incorporated the adaptive capacity of the participants to environmental conditions. The observations are for the period February 2016 - June 2017.
Kongsfjorden 2016-2017 mooring diagram KROP mooring
<div> <p>As part of the KROP - Kongsfjorden Rijpfjorden Observatory Programme, UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain a marine obervatory in the high-Arctic location Kongsfjorden, Svalbard, since 2002. The observatory consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This document contains the mooring diagram for the deployment year 2016-2017. CTD and ADCP data is published at the Arctic Data Centre (<a href="https://adc.met.no/" target="_blank" rel="noopener">https://adc.met.no/</a>). Some previous data is available from NIRD (<a href="https://archive.sigma2.no/">https://archive.sigma2.no/</a>). All metadata is indexed by the SIOS data access portal (<a href="https://sios-svalbard.org/" target="_blank" rel="noopener">https://sios-svalbard.org/</a>).</p> <p>The original diagram has been published together with the dataset in the NIRD data archive. DOI for data and original diagram: <a href="https://doi.org/10.11582/2021.00062" target="_blank" rel="noopener">https://doi.org/10.11582/2021.00062</a></p> <p>The purpose of this re-publication is ease of access to the entire series of mooring diagrams. </p> </div>
Rijpfjorden 2016-2017 mooring diagram KROP mooring
<p>As part of the KROP - Kongsfjorden Rijpfjorden Observatory Programme, UiT The Arctic University of Norway and The Scottish Association for Marine Science maintain a marine observatory in the high-Arctic location Rijpfjorden, Svalbard, since 2006. The observatory consists of an array of CTDs, temperature loggers, ADCPs and a sediment trap, in addition to various other instruments or installations that change from year to year. This document contains the mooring diagram for the deployment year 2015-2016. CTD and ADCP data is published at the Arctic Data Centre (<a href="https://adc.met.no/)">https://adc.met.no/)</a>. Some previous data is available from NIRD (<a href="https://archive.sigma2.no/">https://archive.sigma2.no/</a>). All metadata is indexed by the SIOS data access portal (<a href="https://sios-svalbard.org/">https://sios-svalbard.org/</a>).</p> <p>The original diagram has been published together with the dataset in the NIRD data archive. DOI for data and original diagram: <a href="https://doi.org/10.11582/2021.00020" target="_blank" rel="noopener">https://doi.org/10.11582/2021.00020</a></p> <p>The purpose of this re-publication is ease of access to the entire series of mooring diagrams. </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.