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Temporal and spatial changes of the abundance and species composition of phytoplankton in the California Current from samples collected aboard CalCOFI cruises from summer 1996 through 2022.
The abundances of 385 taxonomic categories of phytoplankton (species where possible) are presented for the 26.5 -year period beginning with summer, 1996 and concluding with autumn 2022. There were four cruises per year. Samples were water samples collected from the second depth, which was designed to sample the mixed layer when a mixed layer existed, generally between 5m - 15m. Before counting, samples from single stations were pooled into four regions: NE (northern inshore), SE (southern inshore), Alley (the region of the California Current) and Offshore (Central Pacific). Pooled samples were enumerated with an inverted microscope. The species data are presented by seven major taxonomic categories followed by the sums of those major taxa. The species codes are defined in the table metadata.
Dissolved trace element concentration profiles of micronutrients (Mn, Ni, Cu, Zn, Co) and contaminants (Cd, Pb) in seawater from discrete bottle samples from CCE Process Cruises in the California Current System, 2021 - 2025 (ongoing).
Dissolved trace element is sampled from the trace metal clean rosette. The sample is collected by filtering seawater through a 0.2µm PES filter. The seawater sample is then acidified to pH~1.8 using ultra clean hydrochloric acid and subsequently analyzed using sector-field inductively coupled plasma-mass spectrometry, scanning in low and medium resolution, with either standard curve or isotope dilution methods. The samples are used to develop a description of the distribution of dissolved trace elements in the CCE region.
Data to support "Marks et al 2016. Assessment of control methods for the invasive seaweed Sargassum horneri in California, USA"
Determining the feasibility of controlling marine invasive algae through removal is critical to developing a strategy to manage their spread and impact. To inform control strategies, we investigated the efficacy and efficiency of removing an invasive seaweed, Sargassum horneri, from rocky reefs on Santa Catalina Island, southern California, USA. We tested the efficacy of removal as a means of reducing colonization and survivorship by clearing S. horneri from 60 m2 circular plots. We also examined whether S. horneri is able to regenerate from remnant holdfasts with severed stipes to determine whether efforts to control S. horneri require the complete removal of entire individuals. In addition, we developed efficiency metrics for manual removal with and without the aid of an underwater suction device. These data have been presented in: Marks, L.M., D.C. Reed and A.K. Obaza. 2016. Assessment of control methods for the invasive seaweed Sargassum horneri in California, USA. Management of Biological Invasions, DOI: 10.3391/mbi.2017.8.2.08, <ulink url="https://doi.org/10.3391/mbi.2017.8.2.08">https://doi.org/10.3391/mbi.2017.8.2.08</ulink>
Kelp metapopulations: Semi-annual time series of giant kelp patch area, biomass and fecundity in southern California, 1996 - 2006
These data describe the patch-scale canopy biomass and population fecundity of giant kelp, Macrocystis pyrifera, in southern California, USA, from 1996¬ to 2007. Biomass of the surface canopy was estimated using diver-calibrated Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus satellite imagery. Fecundity was estimated from canopy biomass pixel data using a seasonally-adjusted relationship between the diver-measured density of giant kelp spore-bearing tissue and the Landsat estimate of canopy biomass density using data collected across five years at the San Clemente Artificial Reef, located offshore of San Clemente, California, USA. Landsat pixel-scale estimates of giant kelp biomass and fecundity were summed across space for each giant kelp patch and averaged across time separately with two semesters each year (January–June and July–December). The location and area of each giant kelp patch are also provided. These data were described in <ulink url="http://dx.doi.org/10.1890/15-0283.1">Castorani, M. C., D. C. Reed, F. Alberto, T. W. Bell, R. D. Simons, K. C. Cavanaugh, D. A. Siegel and P. T. Raimondi. Connectivity structures local populations dynamics: a long-term empirical test in a large metapopulation system. Ecology. DOI: 10.1890/15-0283.1</ulink> These data are part of the NSF collaborative project: The effect of inbreeding on metapopulation dynamics of the giant kelp, Macrocystis pyrifera (funded wholly or part by NSF Awards OCE-1233283, 1233288, 1233839).
Kelp metapopulations: Semi-annual time series of spore dispersal times among giant kelp patches in southern California, 1996 - 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among patches in southern California, USA, from 1996 to 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations among patches were estimated for 6-month periods (January - June and July - Dececember, 1996 - 2006) using minimum mean transit times connecting source and destination connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS). Minimum transport times between giant kelp patches were assumed to be proportional to minimum transport times between ROMS cells and the alongshore distance between giant kelp patches
SBC LTER: REEF: Macrocystis pyrifera blade area and loss in Southern California
These data describe losses of giant kelp (Macrocystis pyrifera) blade tissue observed in the Santa Barbara Channel (Isla Vista Reef) during the summer of 2012. Data are contained in two tables: 1) a time series of measured change in blade area over time at different depths and locations in the kelp forest, 2) modeled estimates of partial blade losses specific to blades on subsurface fronds and blades in the water column and canopy sections of surface reaching fronds.
SBC LTER: Reef: California kelp canopy and environmental variable dynamics
This dataset contains time series of kelp canopy area for giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat satellite imagery along with time series of environmental variables known to be associated with kelp forest dynamics. Data are organized into a single NetCDF file and kelp canopy data are co-located with the nearest environmental data point.
SBC LTER: Nutrient concentrations and algae cover in the Ventura River catchment, California, 2008
Results from these data were reported in: Klose, K., Cooper, S. D., Leydecker, A. D. and Kreitler, J. 2012. Relationships among catchment land use and concentrations of nutrients, algae, and dissolved oxygen in a southern California river. Freshwater Science, 2012, 31(3):908-927 doi: 10.1899/11-155.1 Data not reported here: Chlorophyll-a, physicochemical and land use parameters (e.g., land-use type, water depth, substratum size, % open canopy, and water velocity) were used in the paper's analysis and so were also collected, but are not reported here. Nutrient diffusing substrata (NDS) were deployed at 12 sites to assess the nutrient(s) limiting algal growth; these data are also not reported here. Macroalgal cover and stream nutrients are reported during spring and summer 2008 at 15 stream and estuarine sites in the Ventura River catchment in southern California, USA. Data were collected within a mosaic of undeveloped, agricultural, and urban areas to examine relationships among land use, nutrients, algae, and dissolved oxygen (see paper, linked below). This dataset reports major dissolved nutrients (phosphate, nitrate, ammonium) and total dissolved nitrogen and phosphorus, from May to September 2008, and percent cover of macroalgae (benthic and floating) at the same sites at the beginning and end of this period.
Records of moored SeaFET pH, SeaBird CTD and oxygen at Anacapa, Santa Cruz and San Miguel Islands, California from 2012-2015
Data are pH (total scale, SeaFET), salinity, conductivity, temperature, depth (Seabird 37 CTD) and Oxygen (MicroCAT C-T-ODO) from instruments moored at three sites along the north shores of the northern Santa Barbara Channel Islands, California, USA. At Anacapa Island (ALC), in a marine reserve with kelp forest habitat; at Santa Cruz Island (PRZ), surrounded by a large shallow eelgrass bed (Zostera pacifica); and at San Miguel Island (SMN), in open water over a sandy bottom. pH sensors were deployed at all three sites in 2012, with CTD and Oxyten senosrs added to moorings at ALC and PRZ in May 2013. Benchmark samples for SeaFET sensors calibration were collected 1 to 8 times during each 2 - 3 month deployment via SCUBA, free diving or from a pier with a GO-FLOW (General Oceanics) bottle (see methods). Oxygen data were validated by Winkler titations, with concentrations reported in various units and as saturation. Data are presented in: Kapsenberg, L. and G. E. Hofmann 2016. Ocean pH time-series and drivers of variability along the northern Channel Islands, California, USA. Limnology and Oceanography. 61: 953-968. DOI: 101002/lno.10264.
2017 December California Wildfires Evacuation Survey Data
<p>Following the December Southern California Wildfires in 2017, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the fires in California. Collected from March to July 2018, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 552 valid responses, of which 303 were completed. Subsequent papers using this data retained 226 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of five $200 gift cards. The survey questions are included in a separate PDF document.</p> <p>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</p> <p>Wong, S., Broader, J., Walker, J. & Shaheen, S. (2021). Understanding California Wildfire Evacuee Behavior and Joint Choice-Making. Retrieved from <a href="https://escholarship.org/uc/item/4fm7d34j">https://escholarship.org/uc/item/4fm7d34j</a></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Role of Trust and Compassion in Resource Sharing in Evacuations: A Case Study of the 2017 and 2018 California Wildfire. <em>International Journal of Disaster Risk Reduction. </em><a href="https://escholarship.org/content/qt1zm0q2qc/qt1zm0q2qc.pdf">https://www.sciencedirect.com/science/article/abs/pii/S2212420920314023</a></p> <p>Wong, S., Chorus, C., Shaheen, S. & Walker, J. (2020). A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study. <em>Travel Behaviour and Society.</em> Retrieved from <a href="https://www.sciencedirect.com/science/article/pii/S2214367X19303291"> https://www.sciencedirect.com/science/article/pii/S2214367X19303291</a></p> <p>Wong, S., Broader, J., Shaheen, S. (2020). Review of California Wildfire Evacuations from 2017 to 2019. Retrieved from <a href="https://escholarship.org/uc/item/5w85z07g">https://escholarship.org/uc/item/5w85z07g</a></p> <p>Wong, S. & Shaheen, S. (2019). Current State of the Sharing Economy and Evacuations: Lessons from California. SB 1 Report. Retrieved from <a href="https://escholarship.org/uc/item/16s8d37x">https://escholarship.org/uc/item/16s8d37x</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S. (2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a></p>
California Electric Vehicle Loads by Feeder Circuit
Open the record for dataset details and reuse information.
Customers experiencing a power outage in counties of California, 2019
<p>This dataset includes the long form of the time series from 2019/01/01 to 2019/12/31, showing the number of customers who are experiencing a power outage at the city and county level in the State of California, USA. The time series is divided into 10-min intervals. The original data source is <a href="https://poweroutage.us/">poweroutage.us</a>, a data vendor of power outage data across the United States.</p><p>Power outage long-form time series at the county level are provided in the form of CSV files. Each file contains data for the entire county.</p>
Field-Level California Crop Maps 2007-2021
<p>Field-Level California Crop Maps 2007-2021</p> <p><strong>Abstract</strong></p> <p>Technological advances in satellite image processing have made crop maps readily available over the last decade. Because of the diversity and complexity of crop production in California, however, reliable crop maps for the state are still scant. To fill this gap, we created field-level crop maps of California (hereinafter, Field-Level California Crop Map (FLCCM)) for 2007-2021. We leverage highly accurate ground-truth labels that exist in 2014, 2016, and 2018 to train our crop classifier using probability random forests. We then feed to our classifier the data for predictors that are available from 2007 to 2021. We release three types of crop predictions, and their corresponding accuracy measures in three formats (.csv, .shp, .rds). Our training algorithm can be applied to other settings in which field-level ground-truth data are scarce but fine-resolution pixel-level data are relatively more abundant. </p> <p> </p> <p><strong>Disclamer: </strong>The dataset is in the process of being peer-reviewed. </p>
InSAR stack of San Francisco Bay in California from Sentinel-1 descending track 42 processed with ARIA
<p>A stack of unwrapped interferograms in San Francisco Bay, California, USA from Sentinel-1descending track 42</p> <p>Processor: ARIA (processed using ISCE and prepared using <a href="https://github.com/aria-tools/ARIA-tools">ARIA-tools</a> as shown below)</p> <p>Tropospheric delay estimated from ERA5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.2 (~5 GB):</strong><br> Time: 2015.03.01 - 2022.06.04, 189 acquisitions, 961 interferograms<br> Used ARIA-tools and MintPy commands:</p> <pre><code>ariaDownload.py -b '37.25 38.1 -122.6 -121.75' --track 42 ariaTSsetup.py -f 'products/*.nc' -b '37.25 38.1 -122.6 -121.75' --mask Download prep_aria.py -s ../stack/ -d ../DEM/SRTM_3arcsec.dem -i ../incidenceAngle/*.vrt -a ../azimuthAngle/*.vrt -w ../mask/watermask.msk</code></pre> <p><strong>Version 0.2 (~280 MB; for fast testing of code development)</strong><br> Time: 2016.01.31 - 2017.05.10, 23 acquisitions, 91 interferograms<br> Used ARIA-tools commands (access date Jun 18th, 2022):</p> <pre><code>ariaDownload.py -b '37.35 38.00 -122.45 -121.80' --track 42 --start 20160101 --end 20170510 ariaTSsetup.py -f 'products/*.nc' -b '37.35 38.00 -122.45 -121.80' --mask Download</code></pre> <p> </p>
InSAR stack of the 2019 Ridgecrest, California earthquake sequence from Sentinel-1 descending track 71 processed with ASF HyP3
<p>A stack of unwrapped interferograms on Owens Valley, California for the <a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">2019 Ridgecrest earthquake sequence</a>.</p> <p>Sensor: Sentinel-1 descending track 71</p> <p>Time: 2019.06.10 - 2019.08.15, 7 acquisitions, 11 interferograms</p> <p>Processor: <a href="https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/">ASF HyP3</a> (GAMMA)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy">MintPy</a>.</p>
Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020
<p>Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".</p> <p> </p> <p>Please cite the original paper:</p> <p>Berg, Aleksander K, Dylan S. Connor, Peter Kedron, and Amy E. Frazier. 2024. “Remapping California’s Wildland Urban Interface: A Property-Level Time-Space Framework, 2000–2020.” <em>Applied Geography </em> 167 (June): 103271. https://doi.org/10.1016/j.apgeog.2024.103271.</p> <p><br>WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).</p> <p>All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.</p> <p>Three WUI maps were generated using structures built on of before the years indicated below:</p> <p>2000 - "CA_WUI_2000.tif"</p> <p>2010 - "CA_WUI_2010.tif"</p> <p>2020 - "CA_WUI_2020.tif" </p> <p> </p> <p>Acknowledgments -</p> <p>We thank our reviewers and editors for helping us to improve the manuscript. We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder, Arizona State University, and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. The results and opinions are those of the author(s) and do not reflect the position of Zillow Group. Support by Zillow Group Inc. is acknowledged. We thank Johannes Uhl and Stefan Leyk for their great work in preparing the original dataset. For feedback and comments, we also thank Billie Lee Turner II, Sharmistha Bagchi-Sen, and participants at the 2022 Global Conference on Economic Geography, the 2022 Young Economic Geographers Network meeting, and the 2023 annual meeting of the American Association of Geographers. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.</p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
Supplementary Material for "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems"
<p>This Zenodo repository contains all data, scripts, and supplementary materials for the manuscript entitled, "Invasive plants are associated with increased fire frequency but decreased burn severity in Southern California shrubland ecosystems".</p>
Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California
<div><em><strong>Tectonic uplift, soil production, soil depth, and rock strength at the Dragon's Back Pressure Ridge, Carrizo Plain, California</strong></em></div> <div> </div> <div>Supporting data for “Landscape transience reveals a bottom-up control on soil production”</div> <div> </div> <div>Emily C. Geyman*, David A. Paige, Michael P. Lamb</div> <div> </div> <div>*Corresponding author: Emily C. Geyman, egeyman@caltech.edu</div> <div> </div> <div>Last updated: July 3, 2024</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div><strong>Dataset overview.</strong></div> <div> </div> <div>This dataset contains:</div> <div> </div> <div>1. Georeferenced TIFF files of the (i) LiDAR-derived surface elevation, (ii) geological map (based on the mapping from Dibblee (1973) and Arrowsmith (1995)), (iii) reconstructed cumulative uplift, and (iv) reconstructed uplift rate at the Dragon’s Back Pressure Ridge, Carrizo Plain, California.</div> <div> </div> <div>2. Raw and processed ground penetrating radar (GPR) observations of soil thickness.</div> <div> </div> <div>3. Geomorphic properties: (i) hilltop erosion rate, (ii) hilltop soil production rate, (iii) hilltop saprolite weakness (based on cone penetrometer observations), and (iv) hilltop soil thickness.</div> <div> </div> <div>4. Raw and processed observations from the cone penetrometer (used to compute the saprolite weakness).</div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>6. Matlab code used to perform the MCMC inversion to generate the uplift reconstructions (item (1) above).</div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>Dataset details.</strong></div> <div> </div> <div>See the publication: “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” for details on the field methodology and data analysis. Details about each data product also are provided below. </div> <div> </div> <div> </div> <div>1. Geotiffs.</div> <div> </div> <div>We provide georeferenced TIFF files of the (i) surface elevation, (ii) geological map, (iii) cumulative uplift, and (iv) uplift rate at Dragon’s Back Pressure Ridge, Carrizo Plain, California. The coordinate system for the geotiffs is WGS84 / UTM Zone 11 N (EPSG:32611). All geotiffs are provided at 0.5 m x 0.5 m spatial resolution. Details about each dataset are provided below.</div> <div> </div> <div>(i) Surface elevation. We use LiDAR data from the 2005 B4 Lidar Project, acquired and processed by the National Center for Airborne Laser Mapping (NCALM). The full LiDAR dataset is available for download from OpenTopography (https://portal.opentopography.org/datasetMetadata?otCollectionID=OT.032018.32611.1). We convert the LiDAR point cloud to a 0.5 m gridded bare earth digital elevation model (DEM). </div> <div> </div> <div>(ii) Geological map. The original geological map of Dibblee (1973, 1999) is available from the United States Geological Survey (USGS) at https://pubs.usgs.gov/of/1999/of99-014/. This mapping was refined by Arrowsmith (1995) and Hilley & Arrowsmith (2008). We modify the geological map using high-resolution satellite imagery (from Google, ESRI, and Bing mosaics), as well as high-resolution imagery from the National Agriculture Imagery Program (NAIP), in order to follow the contacts of the Pink, Tan, and Gray members of the Paso Robles Formation. The units on the geological map are coded as:</div> <div>1 - Pink Member, Paso Robles Formation</div> <div>2 - Tan Member, Paso Robles Formation</div> <div>3 - Gray Member, Paso Robles Formation</div> <div>4 - Undifferentiated Paso Robles Formation</div> <div>5 - Quaternary alluvium (older)</div> <div>6 - Quaternary alluvium (younger)</div> <div>7-8 - Quaternary landslides and terraces</div> <div> </div> <div>(iii) Cumulative uplift. We follow the general approach of Hilley & Arrowsmith (2008) to reconstruct the cumulative uplift at Dragon’s Back Pressure Ridge based on the observed positions and elevations of the stratigraphic contacts between the Pink, Tan, and Gray members of the Paso Robles Formation. Put simply, since the Pink, Tan, and Gray members of the Paso Robles Formation are initially flat-lying, the progressive increase in elevation of the contacts between these members from the start to the middle of the Dragon’s Back Pressure Ridge records the cumulative tectonic uplift. We perform a Markov Chain Monte Carlo (MCMC) inversion to reconstruct the uplift history that can best explain our geological observations (i.e., the positions and elevations of the Pink, Tan, and Gray members of the Paso Robles Formation). See section 6 for the Matlab code used to perform the MCMC inversion.</div> <div>Dataset A: “cumulative_uplift_mean.tif” -- the mean reconstructed cumulative uplift (units: meters).</div> <div>Dataset B: “cumulative_uplift_uncertainty_IQR.tif” -- the uncertainty of the reconstructed cumulative uplift (units: meters), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC cumulative uplift estimates.</div> <div> </div> <div>(iv) Cumulative uplift rate. The uplift rate dataset is constructed by taking the spatial derivative of the cumulative uplift dataset (iii) in the along-strike direction of the San Andreas Fault, and then converting from space to time using the long-term slip rate on the San Andreas Fault of approximately 33 mm/yr. This is the same approach as used in Hilley & Arrowsmith (2008).</div> <div>Dataset A: “uplift_rate_mean.tif” -- the mean reconstructed uplift rate (units: mm/yr).</div> <div>Dataset B: “uplift_rate_uncertainty_IQR.tif” -- the uncertainty of the reconstructed uplift rate (units: mm/yr), documented as the inter-quartile range (IQR), the difference between the 75th percentile and the 25th percentile of the MCMC estimates.</div> <div> </div> <div> </div> <div>2. Ground penetrating radar (GPR).</div> <div> </div> <div> <p>The GPR data were acquired with a MALA HDR GPR system with a 450 MHz shielded antenna. Data were acquired every 4 cm, tracked by a survey wheel for precise relative positioning. The GPR survey covered approximately 19 km of ridgeline and included 21 short (approximately 10 m) ridgetop profiles with cone penetrometer observations that serve as ground-truth for the depth of the soil-saprolite boundary inferred from the GPR data. The GPR data were processed using the open-source GPRPy software (Plattner, 2020). We constrained sub-surface velocities by fitting 364 diffraction hyperbolas in the GPR transects. The hyperbola fitting supports a spatially-uniform velocity of approximately 0.11 m/ns. The locations and fitted velocities of the individual hyperbolas used to construct this velocity model are included in the file “GPR_velocities.csv.”</p> <p>The folder “Radar450MHz_raw” includes the raw radar data. The folder “Radar450MHz_GPS” includes the GPS data associated with each radar dataset (saved as .cor files). The GPS observations are aggregated in the spreadsheet “GPS_all” in that folder. The shapefile folder includes the final processed GPR-derived soil thickness estimates (soil thickness reported in units of meters) as a .shp file. The coordinate system for the shapefile is WGS84 / UTM Zone 11 N. </p> </div> <div> </div> <div>3. Geomorphic properties.</div> <div> </div> <div>These are the datasets plotted in Figures 3 and 4 of “Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>4. Cone penetrometer observations.</div> <div> </div> <div> <div>This folder contains 3 files:</div> <div>1. ConePenetrometerSummaryTable_Overview.csv: A summary of the 212 cone penetrometer stations. For each station, there is metadata about the location (GPS coordinates), the stratigraphic unit (Pink, Tan, or Gray Member of the Paso Robles Formation), the side of the ridge, (southeast = SW, center = C, or northwest = NW), and the saprolite weakness, calculated as the cone penetrometer ease of penetration [cm/strike] at the position of the soil-saprolite boundary. </div> <div>2. ConePenetrometerSummaryTable_Data.csv: All of the raw observations from the cone penetrometer. The raw observations are the cumulative strike number vs. the cumulative depth of penetration into the ground.</div> <div>3. ConePenetrometerSummaryTableFinal.xlsx: An Excel spreadsheet with the same data from items (1) and (2) above as separate sheets ("Overview") and ("Data").</div> </div> <div> </div> <div> </div> <div>5. Soil pit observations.</div> <div> </div> <div>This folder contains 2 files:</div> <div>1. soil_pit_summary: A summary table containing the soil pit locations and the inferred depth to the soil-saprolite boundary.</div> <div>2. soil_pit_layers: Simplified stratigraphic columns providing grain sizes and classifications (soil vs. saprolite) of the layers identified in each soil pit.</div> <div> </div> <div> </div> <div>6. Matlab code. </div> <div> </div> <div>This folder contains 2 primary Matlab scripts, with supporting data files and helper functions.</div> <div>1. DBPR_uplift: code to reconstruct the tectonic uplift at DBPR based on the positions and elevations of the stratigraphic contacts.</div> <div>1. soil_depth_vs_strength: code to reconstruct Fig. 4 Geyman, E.C., Paige, D.A., and Lamb, M.P. Landscape transience reveals a bottom-up control on soil production. In review. 2024.” </div> <div> </div> <div> </div> <div>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</div> <div> </div> <div> </div> <div><strong>References</strong></div> <div> </div> <div>Arrowsmith, J. R. Coupled tectonic deformation and geomorphic degradation along the San Andreas Fault System. Ph.D. thesis, Stanford University (1995).</div> <div> </div> <div>Dibblee Jr, T. Regional geologic map of San Andreas Fault and related faults and Carrizo Plain, Temblor, Caliente, and La Panza Ranges and vicinity, California. US Geological Survey Miscellaneous Geological Investigations, Map I-757, scale 1:125,000 (1973). </div> <div> </div> <div>Dibblee, T. W. et al. Regional geologic map of San Andreas and related faults in Carrizo Plain, Temblor, Caliente and La Panza Ranges and vicinity, California: A digital database. Tech. Rep., US Geological Survey (1999). </div> <div> </div> <div>Hilley, G. E. & Arrowsmith, J. R. Geomorphic response to uplift along the Dragon’s Back pressure ridge, Carrizo Plain, California. Geology, 36, 367–370 (2008). </div> <div> </div> <div>Plattner, A. M. GPRPy: Open-source ground-penetrating radar processing and visualization software. The Leading Edge 39, 332–337 (2020).</div> <div> </div>
DATASETS - Pervasive iron limitation at subsurface chlorophyll maxima of the California Current
<p>R and Python code for processing these datasets are available from: 10.5281/zenodo.1495504</p> <p><strong>Abstract: </strong>Subsurface chlorophyll maximum layers (SCMLs) are nearly ubiquitous in stratified water columns and exist at horizontal scales ranging from the submesoscale to the extent of oligotrophic gyres. These layers of heightened chlorophyll and/or phytoplankton concentrations are generally thought to be a consequence of a balance between light energy from above and a limiting nutrient flux from below, typically nitrate (NO3). Here we present multiple lines of evidence demonstrating that iron (Fe) limits or with light colimits phytoplankton communities in SCMLs along a primary productivity gradient from coastal to oligotrophic offshore waters in the southern California Current ecosystem. SCML phytoplankton responded markedly to added Fe or Fe/light in experimental incubations and transcripts of diatom and picoeukaryote Fe stress genes were strikingly abundant in SCML metatranscriptomes. Using a biogeochemical proxy with data from a 40-y time series, we find that diatoms growing in California Current SCMLs are persistently Fe deficient during the spring and summer growing season.We also find that the spatial extent of Fe deficiency within California Current SCMLs has significantly increased over the last 25 y in line with a regional climate index. Finally, we show that diatom Fe deficiency may be common in the subsurface of major upwelling zones worldwide. Our results have important implications for our understanding of the biogeochemical consequences of marine SCML formation and maintenance.</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.