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NOAA NCCOS Assessment: Prioritizing Areas for Future Seafloor Mapping, Research, and Exploration Offshore of California, Oregon, and Washington from 2019-03-01 to 2019-04-01
<p>Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington. </p> <p>This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.</p> <p>There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa <em>et al</em>. 2019.</p>
Water Body Checklists 2019: Gulf of California Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Gulf of California using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Gulf of California Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Gulf of California using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Catalog of Repeating Earthquakes for Northern California, 1984-2014
<p>This catalog includes 27,675 repeating earthquakes grouped in 7,713 sequences in northern California for the years 1984-2014. The repeating earthquakes were identified by a comprehensive analysis of waveform similarity, relative event location, and relative size of all earthquakes recorded by the Northern California Seismic Network (NCSN). Details can be found in:</p> <p>Waldhauser, F., & Schaff, D. P. (2021). A comprehensive search for repeating earthquakes in northern California: Implications for fault creep, slip rates, slip partitioning, and transient stress. Journal of Geophysical Research: Solid Earth, 126, e2021JB022495. https://doi.org/10.1029/2021JB022495</p> <p> </p>
Time-series of shoreline change for the Klamath River Littoral Cell (California)
<p>This repository contains 35 years of tidally-corrected shoreline change data at the Klamath River Littoral Cell in northern California. This dataset was used in <em>Warrick et al. 2023, "</em><strong>A Large Sediment Accretion Wave Along a Northern California Littoral Cell</strong>"<em>, </em>to investigate and track the movement of a large sediment wave.</p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2022. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2014) and a satellite-derived estimate of the beach slope (as described in <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>The data is located in the <em>/shoreline_data</em> folder and structured as follows:</p> <ul> <li>The littoral cell is divided in 4 sections (kmt_01, kmt_02, kmt_03, kmt_04)</li> <li>For each section there is a folder with 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. kmt01-000, kmt01-001 etc). This is the final product used for coastal change analyses.</li> <li><em>time_series_raw.csv</em>: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.</li> <li><em>tide_levels_fes2014</em>: this file contains the tide levels at the time of image acquisition extracted from FES2014 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect.</li> </ul> </li> </ul> <p>In addition, there are 3 geospatial layers (.GEOJSON) which contain important spatial information. All the geospatial layers are in EPSG:2163 - US National Atlas Equal Area:</p> <ul> <li> <em>Klamath_polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each section of the littoral cell.</li> <li><em>Klamath_shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat).</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m alongshore.</li> </ul> <p>Finally, in the<em> /animations</em> folder, there is a clip showing the mapped shorelines on the satellite imagery.</p>
Supplementary Datasets and Movies for the Paper "Major California faults are smooth across multiple scales at seismogenic depth"
<p>Supplementary Datasets and Movies for the Paper<br> <strong>M</strong><strong>ajor California faults </strong><strong>are</strong><strong> smooth </strong><strong>across</strong><strong> multiple scales </strong><strong>at </strong><strong>seismogenic </strong><strong>depth</strong><br> by Anthony Lomax and Pierre Henry</p> <p>DOI: <a href="https://doi.org/10.26443/seismica.v2i1.324">https://doi.org/10.26443/seismica.v2i1.324</a></p> <p>Movies S1-2 Seismicity along the central San Andreas fault zone around Parkfield as Figure 1 in the main text. Shows rotating, lateral view around ~S40°E for (Movie S1) NCSS-DD and (Movie S2) NLL-SSST-coherence. See Figure 1 caption in the main text for key to figure elements.</p> <p>Movie S3-7 Animated, rotating later views of NLL-SSST-coherence relocations of seismicity other than Parkfield presented in the main paper and this supplement.<br> Movie_S1_Parkfield_2022_sect_DD_movie_20230401.mp4 Movie_S2_Parkfield_2022_sect_NLL-SSST-coherence_movie_20230401.mp4 Movie_S3_S_Calaveras_2022_NLL-SSST-coherence_movie_20230401.mp4 – Southern Calaveras Fault Zone<br> Movie_S4_Mendocino_2021_NLL-SSST-coherence_movie_20230401.mp4 – Cape Mendocino<br> Movie_S5_MountLewis_1986_NLL-SSST-coherence_movie_20230401.mp4 – Mount Lewis<br> Movie_S6_SW_SanFrancisco_NLL-SSST-coherence_movie_20230401.mp4 - Southwest of San Francisco<br> Movie_S7_Calipatria_2021_NLL-SSST-coherence_movie_20230401.mp4 - Calipatria sequence</p> <p>Datasets S1-5 CSV format catalogs of NLL-SSST-coherence relocation results presented in the main text.<br> CSV fields correspond to data in the <a href="http://alomax.net/nlloc/soft7.00/formats.html#_location_hypphs_">NLLoc Hypocenter-Phase file</a></p> <ul> <li>ds01_Parkfield_2004_NLL_SSST_coherence.csv - Parkfield</li> <li>ds02_S_Calaveras_2022_NLL_SSST_coherence.csv – Southern Calaveras Fault Zone</li> <li>ds03_Mendocino_2021_NLL_SSST_coherence,csv – Cape Mendocino</li> <li>ds04_MountLewis_1986_NLL_SSST_coherence.csv – Mount Lewis</li> <li>ds05_SW_SanFrancisco_2021_NLL_SSST_coherence.csv - Southwest of San Francisco</li> <li>ds06_Calipatria_2021_NLL_SSST_coherence.csv - Calipatria sequence</li> </ul> <p>File S1 (project_run_scripts.zip) Archive of run scripts and related set-up, configuration and other meta-data files for locations cases presented in this paper.</p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
NLL-SSST-coherence high-precision earthquake location catalog for the 2023 Ojai, California earthquake sequence
<p><strong>Hypocenter catalog files and visualizations of high-precision, NLL-SSST-coherence earthquake locations for the 2023 M5.1 Ojai, California earthquake sequence and background seismicity (2128 events, 1980-01-01 to 2023-08-25).</strong></p> <p>NLL-SSST-coherence (<a href="https://doi.org/10.1029/2021JB023190">Lomax and Savvaidis, 2022</a>; <a href="https://doi.org/10.26443/seismica.v2i1.324">Lomax and Henry, 2023</a>) is an enhanced, absolute-timing earthquake location procedure which 1) iteratively generates spatially varying travel-time corrections to improve multi-scale location precision and 2) uses waveform similarity to improve fine-scale location precision.</p> <p>Relocations performed with phase arrival data available from <a href="http://service.scedc.caltech.edu">http://service.scedc.caltech.edu</a></p> <p>Visualizations include topography from <a href="https://opentopography.org">https://opentopography.org</a> and surface fault traces from <a href="https://usgs.maps.arcgis.com/apps/webappviewer/index.html?id=5a6038b3a1684561a9b0aadf88412fcf">https://usgs.maps.arcgis.com</a></p> <p> </p> <p>This repository contains:</p> <p><strong>Full catalog in CSV format</strong>:<br> CSV file data columns correspond to selected fields of the of NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Full catalog in NonLinLoc hyp format</strong>:<br> NonLinLoc Hypocenter format output <a href="http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_">http://alomax.free.fr/nlloc/soft7.00/formats.html#_location_hypphs_</a></p> <p><strong>Key NLL-SSST-coherence configuration files</strong>: NLL-SSST-coherence_config/*</p> <p><strong>Selected Visualization images</strong></p> <p> </p>
Shell length and dry tissue weight relationship of the mollusk Ostrea lurida (Olympia Oyster) in the low and subtidal zones of the San Diego River, California: 2016-2017.
Data was collected to look at the relationship between the dry tissue weight and the shell length of Ostrea lurida in the San Diego River, and use the data to compare it to other estuaries O. lurida inhabit along the west coast of North America. Sampling was done in the low and sub intertidal zones along the delta of the San Diego River from October 2016 through September 2017. A fifty meter transect was chosen to collect samples which was broken up into five 10 meter transects. In each of these 10-m transects, 0.5 X 0.5 m quadrats were randomly selected for oyster assessments. Oysters were removed by physical removal by hand. Once back in the lab shell lengths were measured and dry weights were obtained.
Temperature Measurements of Southern California Deserts 2022.
The following data was recorded at various desert field sites within Southern California. Data was collected between May 2022 and June 2022. 3 different deserts; Carrizo, Cuyama, and Mojave, were tested. Temperature pendants were deployed for 30 days and recorded local temperature at 1 hour intervals.
California Blue Oak reproduction and recruitment, Sierra Foothills, 2005-2010
This data archive contributes to the study of blue oak conservation by quantifying reproductive success (numbers of new seedlings and their sizes) and survival and growth of the younger life stages (seedlings and saplings), and by exploring how the timing of grazing, topographical variables, and rainfall limit reproduction, growth, and survival for these life stages, helping to identify potential management and restoration strategies to enhance the population viability of blue oak. We followed a population of blue oak (Quercus douglasii) seedlings and saplings in the Sierra Nevada foothills, measuring adult seed production and initial seedling recruitment (number and size of new seedlings), seedling growth and survival, seedling recruitment to saplings (growing greater than 10 cm in height), and sapling growth and survival. We tested the impacts on these demographic processes of timing and intensity of grazing, light availability, water availability, and individual size. We published our analysis of these data as part of the 8th Oak Woodland Symposium. This data archive contains the underlying data for our analysis plus some data we did not include in our analysis (residual dry matter, photosynthetically active radiation, percent exotic/native cover), including raw data and R and Perl scripts used to format them for analysis, and additional information not included in the conference proceedings.
California Department of Fish and Wildlife Enhanced Large Fish Study, San Francisco Estuary, California, 2023 Gillnet Survey
The Enhanced Large Fish Study (ELFS) was established and included in the Interagency Ecological Program (IEP) work plan in 2023 to fill some of the monitoring gap of fishes in the San Francisco Estuary (SFE), California. The fish monitoring of the IEP prior to ELFS primarily consisted of trawl- and seine-based surveys, which generally capture small and/or juvenile fishes due to survey gear and methodologies. In order to more effectively sample the large/adult fish portion of SFE fish communities, the ELFS uses American Fisheries Society experimental gillnets, plus the inclusion of the optional "large fish panel," to sample the waters of the SFE. The experimental gillnets measure 24.4m in length, 1.8m in depth, and includes eight 3m length panels with stretch mesh measurements of 76.2mm, 114.3mm, 50.8mm, 88.9mm, 38.1mm, 127.0mm, 63.5mm, and 101.6mm. The optional large fish panel measures 9.1m by 1.8m in depth and includes three 3m length panels with stretch mesh measurements of 152.4, 177.8, and 203.2mm. The ELFS conducted its first year of sampling in the North Delta, California, in 2023, and will be expanded to the greater Delta and Suisun Bay and Marsh in future sampling years. The ELFS is currently funded as a special study to inform future monitoring, with funding contracted through fiscal year 2026-2027.
Study of wildfire smoke effects on ecosystem metabolism in 10 California lakes (2018, 2020, 2021)
This dataset was collected as part of a large-scale study to assess impacts of smoke cover on gross primary production (GPP) and ecosystem respiration (R) in California lakes. The 10 study lakes span large gradients in elevation, size, nutrient concentrations, and water clarity. They include 5 ponds and lakes in Sequoia National Park, Lake Tahoe, Dulzura Lake, Clear Lake, Castle Lake, and a site in the Sacramento-San Joaquin River Delta. Metabolic rates in the upper mixed layer of each lake was estimated from hourly in-situ sensor data during the three smokiest years in California since 2006 (2018, 2020, 2021). The dataset includes daily estimates of GPP and R, mean daily values of variables used in metabolism models (water temperature, dissolved oxygen, mixed layer depth, photosynthetically active radiation, wind speed), and mean daily values of metrics related to smoke cover (shortwave radiation, PM2.5, smoke density derived from remote-sensing).
Age-0 fish abundances and putative environmental drivers for the Sacramento-San Joaquin Delta, California, 1980 to 2020.
Data on fall (September-December) age-0 abundances of a suite of relatively abundant/common fishes and a suite of likely environmental driver variables. All data are derived from publicly available sources as cited below, except for determination of age-0 maximum length thresholds, which were derived from examination of length-frequency histograms for fall months. Fish data are from https://doi.org/10.6073/pasta/0cdf7e5e954be1798ab9bf4f23816e83. Water temperature, chlorophyll-a, and Secchi depth data are from https://doi.org/10.6073/pasta/42b3d889ffa056030f953aed85b5621e. Zooplankton abundance data are from (https://doi.org/10.6073/pasta/89dbadd9d9dbdfc804b160c81633db0d). Sea surface temperature data are from (https://doi.org/10.6075/J0S75GHD). Ocean upwelling data are from (https://oceanview.pfeg.noaa.gov/products/upwelling/dnld). Flows data are from the DAYFLOW model (https://data.cnra.ca.gov/dataset/dayflow). Environmental driver data are annualized by water year and, where appropriate, aggregated to a single, putatively spatially representative time series.
Data for 'Floral color and family drive contrasting plant-pollinator responses to nutrient enrichment' by Rebecca A. Nelson, Elizabeth T. Borer, and Eric W. Seabloom 2025. Collected in California grasslands 2023 and 2024.
Data for analysis of how flower color and family mediate plant-pollinator response to nutrient enrichment. Data on pollinator visitation and flower abundance were collected in three California grasslands in 2023 and 2024 from a factorial experimental in which combinations of nitrogen, phosphorus, and potassium with micronutrients were applied.
Data for "Pollinator Conservation Paradox: Exotic Forbs Support Native Pollinators Under Global Changes" by Nelson, Seabloom and Borer 2025, California grasslands, 2023-2024
Data for analysis on how plant provenance mediates plant-pollinator interaction responses to fertilization and herbivore exclusion, associated with Nelson, Seabloom, and Borer 2025. Data on pollinator visitation and floral abundance were collected in plots that received factorial experimental treatments of combined nitrogen, phosphorus and potassium with micronutrients by herbivore exclusion fencing in three California grasslands in 2023-2024.
Annual Point Count Breeding Bird Survey at Pepperwood Preserve in the California Coast Ranges 2007-2019
The Dwight Center for Conservation Science at Pepperwood is an ecological institute dedicated to educating, engaging, and inspiring our community through habitat preservation, science-based conservation, leading-edge research, and interdisciplinary educational programs. Our mission is to steward the life and landscapes of the 3,200-acre Pepperwood Preserve and to advance science-based conservation of ecosystems throughout our region and beyond. The Pepperwood Breeding Bird Survey was initiated in the spring of 2007 with the goal of establishing a set of baseline bird community data that would be built upon for years to come. Four transects (totaling 38 points) are surveyed annually using standardized five-minute point count protocols outlined by the Point Reyes Bird Observatory (now called Point Blue Conservation Science; Ballard et al. 2003) and the Handbook of Field Methods for Monitoring Landbirds (Ralph et al. 1993). Surveys are conducted by experienced volunteers during the breeding season starting in late April and ending in June, with each transect surveyed a total of three times. The Rogers Creek and Martin Creek transects were established in 2007. The Weimar Flat and Pepperwood Road transects were established in 2008 and 2012, respectively, to ensure comprehensive coverage across the various habitats that occur at the preserve including Douglas-fir forest, mixed hardwood forest, oak woodland/forest, chaparral, and open grasslands. This dataset includes data collected between 2007-2019.
A micro-environmental dataset exploring shrub-open climate contrasts in Cuyama Valley, California, USA,
A total of 62 Hobo micro-station data logger weather stations were deployed in Cuyama Valley, California drylands on public lands. Each station had 4 channels with two EC5 soil moisture smart sensors and two 12-Bit temperature smart sensors. The sensor arrays were deployed for two years or a total of 24 months. Key ecological variables included microsite, location, vegetation type, and extent of vegetation at each microsite. Temperature sensors were deployed 15 cm above the soil surface under the shrub canopy of Ephedra californica (sheltered from direct sunlight) or at adjacent, open paired-microsites within 1.5m of a focal shrub.Temperatures listed are in degrees Celsius. Soil moisture sensors were inserted into the soil near each temperature probe. The x,y,z columns are the width, perpendicular measure, and height in m respectively of each shrub at those microsites.
Post-Tubbs Fire Chaparral Floristic Survey at Pepperwood Preserve in the California Coast Ranges 2018-2019
The Dwight Center for Conservation Science at Pepperwood is an ecological institute dedicated to educating, engaging, and inspiring our community through habitat preservation, science-based conservation, leading-edge research, and interdisciplinary educational programs. Our mission is to steward the life and landscapes of the 3,200-acre Pepperwood Preserve and to advance science-based conservation of ecosystems throughout our region and beyond. Following the October 2017 Tubbs Fire, Pepperwood hired Nomad Ecology, LLC, to implement Nomad Ecology's post-fire research program at Pepperwood. Specifically, Nomad Ecology conducted a two-year study of post-fire plant diversity and succession in chaparral at the preserve. Species richness and ecological dynamics are not well understood in these post-fire areas (especially in northern California) despite high interest from land managers, ecologists, and botanists. Documentation of the post-fire flora and the sensitive species that are part of this fleeting diversity is essential to understanding the full range of natural resources associated with chaparral ecosystems, and thus key to developing conservation goals specific to Pepperwood. This study documented the burn severity and diversity and abundance of the fleeting post-Tubbs Fire flora in spring 2018 and 2019 using species ocular cover estimates across ten 50-meter belt transects in four different soil types: rhyolite, fluvial and lacustrine deposits, andesite, and serpentinite.
Picophytoplankton and bacteria abundances analyzed with flow cytometry (FCM) from CCE-CalCOFI Augmented cruises in the California Current System, 2004 - 2023 (ongoing).
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from 3 to 8 depths at CalCOFI stations. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact.
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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.