This U.S. Geological Survey data release consists of a polygon geospatial dataset representing estimated flood-inundation areas in Grapevine Canyon near Scotty's Castle, Death Valley National Park, and the data acquired and processed to support the delineation of those areas. Supporting datasets include topographic survey data collected by global navigation satellite system (GNSS) and terrestrial laser scanner (TLS) in Grapevine Canyon from July 12-14, 2016; derivatives of those data; pebble count data collected in Grapevine Canyon; and an archive of the one-dimensional hydraulic model used to generate the flood-inundation area polygons. Specifically: 1)a point dataset of four static reference locations (StaticGNSS_x) collected by single-baseline Online Positioning User Service – Static (OPUS-S) GNSS surveys; 2)a point dataset of 38 TLS survey scan locations (ScanOrigins_x) collected by real-time kinematic (RTK) GNSS surveys; 3)a zip file of 42 point cloud files (GrapevineCanyon_LAZ.zip) collected at 38 scan locations by TLS surveys; 4)a point dataset of 769 ground control points (GroundControlPts_x) collected by RTK GNSS surveys; 5)a point dataset of filtered ground observations (TLS_FilteredGroundObs_x) from the TLS surveys; 6)a polygon dataset of the areas used to filter the ground observations (TLS_Filter_p); 7)a digital terrain model (GrapevineCanyon_TIN.zip) derived from the filtered ground observations as a triangulated irregular network (TIN) in North American Vertical Datum of 1988; 8)a comma-separated values (CSV) table of the locations and results of five Wohlman-style pebble counts (Wolman, 1954), collected at five sites within the study area (GrapevineCanyon_PebbleCounts.csv); 9)a zip file containing all relevant files to document and run the Hydrological Engineering Center-River Analysis System (HEC-RAS) one dimensional hydraulic model used to generate the flood-inundation area polygons (SWmodel_Archive.zip); 10)a polygon dataset of the estimated flood-inundation areas (GrapevineCanyonInundationAreas_p).

These are data on vegetation (trees and understory) from five (1 ha) permanent sample plots in pine-dominated (Pinus contorta) forests in Banff and Jasper National Parks (Alberta, Canada) including data from three time periods: 1967, 1989, and 2010s and soils data collected in 1967 and 1968. Data were collected in contiguous quadrats (5m x 5m) set up as a spatially structured grid in each plot. The files are organized in folders, visible with the 'tree' option under the 'files' tab. These folders are: *1_Overview_and_metadata: has 1a_Summary_quick-start.pdf (a concise summary of the project), 1b_ReadMe_Summary.txt (a detailed description), 1c_data_dictionary.csv (a list of all data files, including variables and metadata); *2_Publications: has .pdf files, including a list of publications from the data and the three graduate theses; *3_Plot_setup_and_sampling_design: has .pdf files with diagrams of the plot setup at each site and five .csv files with details on plot sampling and topographic data; *4_Vegetation_data_files: has four .csv files with tree and understory vegetation data; *5_Dendrochronology_data_files: has 15 files with dendrochronology data by species for each site;*6_Soils_data_files: has four files with data on soil chemical and physical properties. This project was conducted with the financial and technical assistance of the Canadian Institute of Ecology and Evolution’s Living Data Project. The LDP is funded by a grant from the Natural Sciences and Engineering Research Council of Canada’s (NSERC) Collaborative Research and Training Experience (CREATE) program. This project was mentored by David Hunt and Sally Taylor.

The National Park Service’s Arctic Inventory and Monitoring Network (ARCN) encompasses five park units in northwestern Alaska: Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park (GAAR), and Noatak National Preserve. Tasked with providing scientific information for Park decision-making, ARCN identified fish as a key vital sign for ecological monitoring because fish play an essential role in these park ecosystems — they are a primary food source for Indigenous communities living in and near the region, they occupy critical positions within aquatic food webs, and they often serve as top predators in lakes and streams. Moreover, fish are highly susceptible to climate change, which poses a significant threat to Arctic ecosystems worldwide. Despite their ecological and cultural importance, limited data exist on fish populations within the Arctic Network. To address this gap, the dataset described here provides baseline information on fish in GAAR, consisting of capture data collected from the Noatak River in Gates of the Arctic National Park and Preserve between July 15 and 26, 2005 (Bowden et al., 2005). Key words Fish, Alaska, Gates of the Arctic, Noatak,Arctic grayling,Lake trout,Nine-spine stickleback,Northern pike,Round whitefish,Spiny sculpin,Anorat Creek,Kipmik Lake,Komakak Creek,Kugrak Spring,Lake Matcharak,Nushralutak Creek,Oyukak Creek Files in this Release This data release consists of two data files: 2005 GAAR Fish Sampling.csv 2005 GAAR Fish Sampling.csv_Metadata.csv Metadata Column names, descriptions and units of measure for the data file 2005 GAAR Fish Sampling.csv appears in Table 1. Table 1. ColumnName DataType Description UnitsOfMeasure Date Date Date. Name character Formal name, if known. CommonName character Common name. TL (mm) numeric Description unknown. Probably Total Length. Millimeters Weight (g) numeric Weight Grams PERSON OR NET HOURS numeric Person or net hours. Hours Sculpin trap hours character Sculpin trap hours Hours Disposition character Disposition Fish fate. Lat numeric Latitude Decimal degrees,geographic coordinate system, WGS84. Long numeric Longitude Decimal degrees,geographic coordinate system, WGS84. Notes character Notes Site character Site Gear character Gear used to collect and observe fish. Methods NPS personnel sampled lakes and streams for fish in GAAR from July 15 and 26, 2005 using various techniques, including angling, trapping, direct observation, and nets (gill, kick, and sweep). Sampling intensity was documented in terms of person-hours or net-hours. Length (mm) and weight (g) measurements were recorded for many, though not all specimens. Sampling locations were recorded in numerous cases, but for others, no location data exists other than unofficial names. Sampling intensity was likely not sufficient to determine a complete list of fish species for any site. Study Area Fish were sampled in water bodies, mostly in the Noatak River basin in Gates of the Arctic National Park and Preserve, Alaska, U.S.A.

This dataset contains timelapse videos and distributed measurements of air temperature, relative humidity, dewpoint, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed across 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by lead-PI Dr. Jessica Lundquist (University of Washington), and co-PIs Dr. Rosemary Carroll (Desert Research Institute) and Dr. Ethan Gutmann (National Center for Atmospheric Research). This project will use these surface data in hydrologic models to better resolve the fate of mountain water. Data are made available for other projects interested in surface climate or hydrologic processes in complex terrain. Measurements are taken with low-cost data loggers strapped to trees or buried just below the soil surface. The timelapse video captures images 3x daily images across one transect and provides insight into valley-scale seasonal snow cover variability. Files contained in this dataset are named and organized by site and variable type (air measurements, ground measurements, or timelapse video). Air and ground measurements are packaged in LoggerData.zip, and the timelapse video is stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. Locations metadata describes site characteristics, locations, and associated GPS methods.

These are data on vegetation (tree size, age, density; understory vegetation) and soil (nutrients, physical properties, moisture, classification) from 63 stands of lodgepole pine-dominated forest in Banff and Jasper National Parks, Alberta, Canada. Data were collected in 1967 - 1968 as an extensive survey. From these, five stands were selected for intensive study and were subsequently sampled repeatedly over time. Those data are available in another data-set ( https://doi.org/10.5683/SP3/YAQCWD ). The files are organized in folders, visible with the 'tree' option under the 'files' tab. These folders are: *1_Metadata_and_Overview: has Summary_quick-start.pdf (a concise summary of the project); ReadMe_Summary.txt (a detailed description of data files and meta-data); data_dictionary.csv (a list of all data files, including variables and metadata); general_characteristics_location (location and general topographic characteristics of the stands); *2_Publications: has .pdf versions of the thesis and publication in which these data were originally published. *3_Data_files: has .csv files with data on the trees (4 files), soils (3 files), and understory vegetation (3 files). This project was conducted with the financial and technical assistance of the Canadian Institute of Ecology and Evolution’s Living Data Project. The LDP is funded by a grant from the Natural Sciences and Engineering Research Council of Canada’s (NSERC) Collaborative Research and Training Experience (CREATE) program. This project was mentored by David Hunt and Sally Taylor.

Wind, sediment transport and surface morphological data collected at Sand Creek during a month long field campaign in March and April 2019 to investigate protodune development under bimodal winds. Data is used in the accepted paper ‘Dune initiation in a bimodal wind regime’, Journal of Geophysical Research: Earth Surface, by Delorme, P., Wiggs, G.F.S., Baddock, M.C., Claudin, P., Nield, J.M. and Valdez, A. (accepted 18th September 2020, article reference number 2020JF005757R; https://repository.lboro.ac.uk/articles/Dune_initiation_in_a_bimodal_wind_regime/12973817) Surface morphological data: This is terrestrial laser scanned (TLS) data collected of the creek sand surface during multiple visits. The data is raw point cloud format in text columns of x, y and z coordinate data. It has been orientation in local format (the origin is located at 13UTM 443152, 4184478). *_full_lowres cover the whole creek surface and the banks on either side. * is the date that the data was collected in yymmdd format. All other data is high resolution section of the actual creek surface within the channel. Each data set uses the same coordinate system. Data can be viewed in any spatial software. Wind and sediment data were collected from a fixed point on the eastern edge of the creek channel. The data is in csv file format with column titles and can be viewed in any text or database software. See Delorme et al. (accepted) for more details.

The National Park Service’s Arctic Inventory and Monitoring Network (ARCN) encompasses five park units in northwestern Alaska: Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park (GAAR), and Noatak National Preserve. Tasked with providing scientific information for Park decision-making, ARCN identified fish as a key vital sign for ecological monitoring because fish play an essential role in these park ecosystems — they are a primary food source for Indigenous communities living in and near the region, they occupy critical positions within aquatic food webs, and they often serve as top predators in lakes and streams. Moreover, fish are highly susceptible to climate change, which poses a significant threat to Arctic ecosystems worldwide. Despite their ecological and cultural importance, limited data exist on fish populations within the Arctic Network. To address this gap, the dataset described here provides baseline information on fish in GAAR, consisting of capture data collected from the Noatak River in Gates of the Arctic National Park and Preserve between July 15 and 26, 2005 (Bowden et al., 2005). Key words Fish, Alaska, Gates of the Arctic, Noatak,Arctic grayling,Lake trout,Nine-spine stickleback,Northern pike,Round whitefish,Spiny sculpin,Anorat Creek,Kipmik Lake,Komakak Creek,Kugrak Spring,Lake Matcharak,Nushralutak Creek,Oyukak Creek Files in this Release This data release consists of two data files: 2005 GAAR Fish Sampling.csv 2005 GAAR Fish Sampling.csv_Metadata.csv Metadata Column names, descriptions and units of measure for the data file 2005 GAAR Fish Sampling.csv appears in Table 1. Table 1. ColumnName DataType Description UnitsOfMeasure Date Date Date. Name character Formal name, if known. CommonName character Common name. TL (mm) numeric Description unknown. Probably Total Length. Millimeters Weight (g) numeric Weight Grams PERSON OR NET HOURS numeric Person or net hours. Hours Sculpin trap hours character Sculpin trap hours Hours Disposition character Disposition Fish fate. Lat numeric Latitude Decimal degrees,geographic coordinate system, WGS84. Long numeric Longitude Decimal degrees,geographic coordinate system, WGS84. Notes character Notes Site character Site Gear character Gear used to collect and observe fish. Methods NPS personnel sampled lakes and streams for fish in GAAR from July 15 and 26, 2005 using various techniques, including angling, trapping, direct observation, and nets (gill, kick, and sweep). Sampling intensity was documented in terms of person-hours or net-hours. Length (mm) and weight (g) measurements were recorded for many, though not all specimens. Sampling locations were recorded in numerous cases, but for others, no location data exists other than unofficial names. Sampling intensity was likely not sufficient to determine a complete list of fish species for any site. Study Area Fish were sampled in water bodies, mostly in the Noatak River basin in Gates of the Arctic National Park and Preserve, Alaska, U.S.A.

The goal of this study was to understand the change in reflectance caused by the action of fire and the heterogeneity of fire effects (i.e., the fraction of the observation that burned and the combustion completeness of that observation). A spectral mixture model and field and satellite observations were used to compare changes in Landsat reflectance associated with fire and combustion completeness derived from field measurements at prescribed fire sites in South Africa and to substantiate and illustrate the model findings.Fire residue samples were collected from experimental burn plots in Kruger National Park during the SAFARI 2000 Dry Season Campaign in August-September of 2000. The residues include 3 different ash types: pure white ash; black ash; and residual non-photosynthetic fuel biomass (senescent grass, twigs, leaves, and bark). The samples were analyzed in the laboratory to determine their multi-spectral reflectance using an Analytic Spectral Devices (ASD) radiometer, which measures spectral reflectance in the range 0.45-2.2 um at intervals of 0.01 um. The ASD measurements were made under diffuse illumination conditions in the spectral range of 350 nm to 2500 nm, with the ASD radiometer aligned perpendicular to the samples to simulate nadir remote sensing conditions.The data file contains reflectance measurements (fraction, 0-1) for replicate samples of 3 different ash types (white ash, black ash, and non-photosynthetic vegetation) recorded at 10 nm intervals over the wavelength spectral range of 350 nm to 2500 nm. The data are stored in a single ASCII file in comma-separate-value format (.csv).

Regions with remote and complex terrain experience spatially varying streamflow patterns, but are often poorly sampled due to difficult access. This data package includes streamflow measurements collected using low-visibility and low-impact installations at four sites on the Tuolumne River in Yosemite National Park, for water years 2002 to 2021. The resulting data set offers a unique opportunity to explore hydrologic processes in complex terrain. This data package contains half-hourly recordings of unvented pressure, vented pressure, and water temperature are measured and used to estimate discharge and stage height. Discharge flags provide insight into data anomalies. This dataset is formatted in accordance with ESS-Dive's Hydrologic Monitoring and File Level Metadata Formats. It contains the following files: 1) Folder containing four csv files of time series streamflow measurements (unvented pressure, vented pressure, estimated discharge, water temperature, stage height, and discharge flag) from four locations on the Tuolumne River 2) Data dictionary (dd.csv) containing units, definitions, human readable column names, and data type for all column headers throughout the dataset 3) File-level metadata (FLMD.csv) containing metadata for files contained in the dataset 4) Installation methods (InstallationMethods.csv) containing metadata on sensor installation

Subaerial and submarine landslides adjacent to, and within, Glacier Bay, Glacier Bay National Park and Preserve (GBNPP), Alaska pose a threat to the public because of their potential to generate ocean waves that could impact marine activities. Although historical records of tsunamis generated by landslides in GBNPP are uncommon, there are records that document the destructive power of at least three landslide-generated tsunamis in Lituya Bay on the west side of the park (Miller, 1960; Fritz, 2001). Additionally, the threat of a rapid failure of a slow-moving subaerial bedrock landslide in Tidal Inlet off the West Arm of Glacier Bay has drawn attention because of its tsunamigenic potential that could affect boat and cruise-ship traffic in the West Arm (Wieczorek and others, 2007). The largest historical subaerial landslide in GBNPP was the June 28, 2016 Lamplugh rock avalanche with a volume of about 70 M m3 (Bessette-Kirton and others, 2018; Dufresne and others, 2019). This rock avalanche did not enter the water of Glacier Bay, but instead was deposited on the Lamplugh Glacier. As part of a broad effort by the U.S. Geological Survey and National Park Service to evaluate landslide and tsunamigenic potential throughout GBNPP (for example, Coe and others, 2018; Coe and others, 2019; Avdievitch and Coe, 2022; Kim and others, 2022; Hults and others, 2023), we assessed rock mass quality and collected structural geology data in a large part of Glacier Bay National Park and Preserve in the summers of 2021 and 2022. The quality (strength) of a rock mass depends on the properties of intact rock and the characteristics of discontinuities (for example, bedding, fractures, cleavage) that cut the rock. Rock mass quality can be estimated in the field using a variety of classification schemes. Our fieldwork was primarily boat-based and was therefore conducted at sites along and near the coastline. A small number of sites were accessed by hiking. At each field site, we made our measurements at rock outcrops that were typically found at the base of cliffs, along ridge lines, in flat areas in coastal zones, and in areas recently scoured and plucked by glaciers. In two dimensions, outcrops ranged in size from about 30 m2 to 100 m2. We visited a total of 57 sites in the field. Sites occurred within a variety of geologic units (Brew, 2008; Wilson and others, 2015; National Park Service, 2020). Specific geologic units mentioned in our data files are from a geologic map compilation by National Park Service (2020). Of the 57 sites, we collected rock mass quality data and structural data at 54 sites, and only strike and dip of bedding or fractures at 3 sites. At each of the 54 sites, we collected data that we later used to classify rock mass quality according to four commonly used classification schemes; Rock Mass Quality (Q, for example, Barton and others, 1974, Coe and others, 2005); Rock Mass Rating (RMR, for example, Bieniawski, 1989); Slope Mass Rating (SMR, for example, Romana, 1995, Moore and others, 2009) and Geologic Strength Index (GSI, for example, Marinos and Hoek, 2000, Marinos and others, 2005). We also determined Rock Quality Designation (RQD, for example, Deere and Deere, 1989, Palmström, 1982) and estimated intact rock strength using a Proceq Rock Schmidt Type N hammer (see RatingsReadMe.pdf for details). Schmidt hammer rebound values were converted to Uniaxial Compressive Strength (UCS) using equations developed for the same rock types that we observed in the field, but at different locations. For non-limey sedimentary and metasedimentary rocks, rebound values from the Type N Schmidt hammer were converted to UCS by first converting Type N rebound values to Type L rebound values using equation 25 in Asteris and others (2021), then using these Type L values in the equation shown in Table 3 and Figure 3 of Morales and others (2004). For intrusive igneous rocks, marble, and limestone, UCS values were calculated using Type N rebound values in equation 2 of Katz and others (2000). For extrusive igneous rocks, UCS values were calculated using Type L rebound values in the equation for igneous rocks listed in Table 2 of Karaman and Keismal (2015). Additionally, we collected strikes and dips of any observed bedding, fractures, and cleavage. All four rock mass quality classification schemes use data from characteristics of discontinuities present in the rock. Discontinuity data that we collected in the field included: total number of discontinuities, roughness of the surface of the discontinuities, number of sets of discontinuities, type of filling or alteration on the surface of discontinuities, aperture or “openness” of discontinuities, and the amount of water present. A file of a blank field data collection sheet (FieldDataCollectionSheet) is included in this data release. Numerical ratings for each of these factors are assigned based on the correlation of field measurements and observations with descriptive rankings. The rankings used for Q, RMR, SMR, and GSI classification schemes are shown in Table 1, Table 2, Table 3, Figure 1, and Figure 2 (available in a zip file named FiguresandTables.zip). Additional details regarding descriptive rankings and numerical ratings not shown in the tables and figures are given in the RatingsReadMe.pdf. All field measurements, numerical ranking values, and calculated Q, RMR, SMR, GSI, and RQD values are given in the RMQMeasurements_Ratings_Values20212022 file (.csv and .xlsx). Site names beginning with “JAC”, followed by numbers, are locations where both rock mass quality and structural data were collected. Site names beginning with “JACSD” are locations where only the strike and dip of bedding was measured. Question marks in the data files indicate a lack of certainty in field observations. Abbreviations of rating parameters (for example, R4e, Jw, etc.) for the RMR, SMR, and Q classification systems used in column headings are defined in more detail in Tables 1 and 2. All structural measurements are given in the StructuralData20212022 file (.csv and .xlsx). The planar and toppling calculations used for determining SMR values are given in the SMRCalculationsWorksheet20212022 file (.csv and .xlsx). Final Q, RMR, SMR, GSI, and RQD values for each site are presented in a separate file (FinalRockStength_QualityValues20212022, .csv and .xlsx). All rock mass quality values are positively correlated with rock quality. That is, as Q, RMR, SMR, GSI, and RQD values increase, rock quality increases. Photos from each site are included in a separate folder (20212022PhotosbySiteName), organized by the individual site names and the names of the photographers. A Google Earth file, GBSiteNameCoords20212022.kml, showing site locations, site names, and geographic coordinates is also included. We thank National Park Service research vessel captain Justin Smith for his expert guidance and patience during fieldwork in the summers of 2021 and 2022. Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Asteris, P.G., Mamou, A., Hajihassani, M., Hasanipanah, M., Koopialipoor, M., Le, T.-T., Kardani, N., and Armaghani, D.J., 2021, Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks: Transportation Geotechnics, v. 29, p. 100588, https://doi.org/10.1016/j.trgeo.2021.100588 Avdievitch, N.N. and Coe, J.A., 2022, Submarine landslide susceptibility mapping in recently deglaciated terrain, Glacier Bay, Alaska: Frontiers in Earth Science, v. 10, 821188, https://doi.org/10.3389/feart.2022.821188 Barton, N., Lien, R., and Lunde, J., 1974, Engineering classification of rock masses for the design of tunnel support: Rock Mechanics, v. 6, p. 189-236. https://doi.org/10.1007/BF01239496 Bessette-Kirton, E.K., Coe, J.A., and Zhou, W., 2018, Using stereo satellite imagery to account for ablation, entrainment, and compaction in volume calculations for rock avalanches on glaciers: Application to the 2016 Lamplugh rock avalanche in Glacier Bay National Park, Alaska: Journal of Geophysical Research - Earth Surface, v. 123, no. 4, p. 622-641. https://doi.org/10.1002/2017JF004512 Bieniawski, Z.T., 1989, Engineering rock mass classifications a complete manual for engineers and geologist in mining, civil, and petroleum engineering: John Wiley & Sons, New York, 251 p. Brew, D. A., 2008, Delineation of Landform and Lithologic Units for Ecological Landtype-Association Analysis in Glacier Bay National Park, Southeast Alaska. U.S. Geological Survey Scientific Investigations Report 2008-5183. https://pubs.usgs.gov/sir/2008/5183/ Coe, J.A., Harp, E.L., Tarr, A.C., and Michael, J.A., 2005, Rock-fall hazard assessment of Little Mill campground, American Fork Canyon, Uinta National Forest, Utah: U.S. Geological Survey Open File Report 2005-1229, 48 p., two 1:3000-scale plates. http://pubs.usgs.gov/of/2005/1229/ Coe, J.A., Bessette-Kirton, E.K., and Geertsema, M., 2018, Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve, Alaska detected from 1984 to 2016 Landsat imagery: Landslides, v. 15, no. 3, p. 393-407, https://link.springer.com/article/10.1007/s10346-017-0879-7 Coe, J.A., Schmitt, R.G., and Bessette-Kirton, E.K., 2019, An initial assessment of areas where landslides could enter the West Arm of Glacier Bay, Alaska and implications for tsunami hazards: Alaska Park Science, v. 18, no. 1, p. 26-37, https://www.nps.gov/articles/aps-18-1-4.htm Deere, D.U., and Deere, D.W., 1989, Rock Quality Designation (RQD) after twenty years: Contract Report GL-89-1, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss., 25 p. Dufresne, A., Wolken, G., Hilbert, C., Bessette-Kirton, E.K., Coe, J.A., Geertsema, M., and Ekstrom, G., 2019, The 2016 Lamplugh landslide: Alaska: deposit structures and emplacement dynamics: Landslides, v. 16, no. 12, p.

Summary: Siliceous sinter samples were collected from multiple geysers in the Upper Geyser Basin of Yellowstone National Park in 2018. These geyserite samples were collected and analyzed as a part of a multi-year research investigation into the age and geochemistry of hydrothermal features in the Upper Geyser Basin. Samples were collected along the stratigraphy of each feature. From these samples, we report 10Be, U-series, and 14C ages. Samples collected from Giant and Castle Geyser were further analyzed for their mineralogy, major and trace element concentrations, water content, and rare earth elements. This research was conducted under Yellowstone Research Permits YELL-2018-SCI-8030 and YELL-2018-SCI-5910. Sample Collection: Sinter samples were collected from the Upper Geyser Basin of Yellowstone National Park in April 2018, November 2018, and April 2019. Forty-eight samples of silica sinter were collected from 10 different hydrothermal features. At each geyser the best-exposed stratigraphy was identified and fist-sized samples were collected at approximately 1 meter intervals from the shield to the cone; samples were numbered sequentially. Geographic coordinates (WGS84, UTM Zone 12N) of each sample site were obtained using a handheld GPS and all sites were photographed. Including duplicates, we report 46 total ages using carbon-14, beryllium-10, and uranium-thorium series dating methods. Database Contents: The data file (Sample_Information_Supplementary.csv) contains the sample ID, date collected, and sample _location for each sample analyzed as a part of this study. The entries in the data file appear in the following columns: A. Sample ID B. Location C. Date Collected D. Easting (WGS84, UTM Zone 12N) E. Northing (WGS84, UTM Zone 12N)

This category of information refers to the data or information related to a specific occurrence of a taxon (usually a species), whether in nature, in a collection or in a data set.Darwin Core is a set of standards developed and promoted by the international organization TDWG (Biodiversity Information Standards) and used by the network of the Global Biodiversity Information Infrastructure (GBIF) to facilitate the exchange of information on biological diversity.Darwin Core Archive (DwCA) is a compressed file (Zip), composed of several information files:Flat text file with the information, separated by a separator character (coma, dot and coma or tabulator). It is a file in XML format, which complies with the GMP standard (GBIF Metadata Profile), based on the EML standard (Ecological Metadata Language).Descriptor file (meta.xml), which describes the organization of the previous files. It is a file in XML format that indicates how the information of the previous files is organized, as well as the composition of the compressed file.In addition to Darwin Core format, the information is offered in excel and csv.

This category of information refers to the data or information related to a specific occurrence of a taxon (usually a species), whether in nature, in a collection or in a data set.Darwin Core is a set of standards developed and promoted by the international organization TDWG (Biodiversity Information Standards) and used by the network of the Global Biodiversity Information Infrastructure (GBIF) to facilitate the exchange of information on biological diversity.Darwin Core Archive (DwCA) is a compressed file (Zip), composed of several information files:Flat text file with the information, separated by a separator character (coma, dot and coma or tabulator). It is a file in XML format, which complies with the GMP standard (GBIF Metadata Profile), based on the EML standard (Ecological Metadata Language).Descriptor file (meta.xml), which describes the organization of the previous files. It is a file in XML format that indicates how the information of the previous files is organized, as well as the composition of the compressed file.In addition to Darwin Core format, the information is offered in excel and csv.

This category of information refers to the data or information related to a specific occurrence of a taxon (usually a species), whether in nature, in a collection or in a data set.Darwin Core is a set of standards developed and promoted by the international organization TDWG (Biodiversity Information Standards) and used by the network of the Global Biodiversity Information Infrastructure (GBIF) to facilitate the exchange of information on biological diversity.Darwin Core Archive (DwCA) is a compressed file (Zip), composed of several information files:Flat text file with the information, separated by a separator character (coma, dot and coma or tabulator). It is a file in XML format, which complies with the GMP standard (GBIF Metadata Profile), based on the EML standard (Ecological Metadata Language).Descriptor file (meta.xml), which describes the organization of the previous files. It is a file in XML format that indicates how the information of the previous files is organized, as well as the composition of the compressed file.In addition to Darwin Core format, the information is offered in excel and csv.

The use of high-resolution remotely sensed imagery can be an effective way to obtain quantitative measurements of rock-avalanche volumes and geometries in remote glaciated areas, both of which are important for an improved understanding of rock-avalanche characteristics and processes. We utilized the availability of high-resolution (~0.5 m) WorldView satellite stereo imagery to derive digital elevation data in a 100 km2 area around the 28 June 2016 Lamplugh rock avalanche in Glacier Bay National Park and Preserve, Alaska. We used NASA Ames Stereo Pipeline, an open-source software package available from NASA, to produce one pre- and four post-event digital elevation models (DEMs) of the area surrounding the Lamplugh rock avalanche. This data release includes five raster elevation datasets (2-m resolution) in GeoTIFF format that have been orthrectified to the Universal Transverse Mercator (UTM) coordinate system (zone 7N). Elevations are measured in reference to the World Geodetic System 1984 (WGS84) ellipsoid. Because the study area is remote and difficult to access, ground control was not available to assess the absolute accuracy of DEMs. The DEMs have not been precisely co-registered. Data contained in this release include a pre-event DEM from 15 June 2016, and post-event DEMs from 16 July 2016, 27 August 2016, 27 September 2016, and 28 September 2016. The filenames for these DEMs are 20160615_LamplughDEM.tif, 20160716_LamplughDEM.tif, 20160827_LamplughDEM.tif, 20160927_LamplughDEM.tif, and 20160928_LamplughDEM.tif, respectively. We also provide a CSV file (Lamplugh_DEM_Image_Notes.csv) that contains the acquisition date, satellite platform, image identification number, resolution, off-nadir angle, and notes on image quality for each stereo pair used to generate DEMs.

This category of information refers to the data or information related to a specific occurrence of a taxon (usually a species), whether in nature, in a collection or in a data set.Darwin Core is a set of standards developed and promoted by the international organization TDWG (Biodiversity Information Standards) and used by the network of the Global Biodiversity Information Infrastructure (GBIF) to facilitate the exchange of information on biological diversity.Darwin Core Archive (DwCA) is a compressed file (Zip), composed of several information files:Flat text file with the information, separated by a separator character (coma, dot and coma or tabulator). It is a file in XML format, which complies with the GMP standard (GBIF Metadata Profile), based on the EML standard (Ecological Metadata Language).Descriptor file (meta.xml), which describes the organization of the previous files. It is a file in XML format that indicates how the information of the previous files is organized, as well as the composition of the compressed file.In addition to Darwin Core format, the information is offered in excel and csv.

This category of information refers to the data or information related to a specific occurrence of a taxon (usually a species), whether in nature, in a collection or in a data set.Darwin Core is a set of standards developed and promoted by the international organization TDWG (Biodiversity Information Standards) and used by the network of the Global Biodiversity Information Infrastructure (GBIF) to facilitate the exchange of information on biological diversity.Darwin Core Archive (DwCA) is a compressed file (Zip), composed of several information files:Flat text file with the information, separated by a separator character (coma, dot and coma or tabulator). It is a file in XML format, which complies with the GMP standard (GBIF Metadata Profile), based on the EML standard (Ecological Metadata Language).Descriptor file (meta.xml), which describes the organization of the previous files. It is a file in XML format that indicates how the information of the previous files is organized, as well as the composition of the compressed file.In addition to Darwin Core format, the information is offered in excel and csv.

Biodiversity loss is a pressing challenge with ecosystems across the world under threat from factors such as human encroachment, over exploitation and climate change. It is important to increase ecosystem monitoring efforts to provide actionable insights for ecosystem managers and to allow effective use of conservation resources. This dataset is used to compare traditional bird survey approaches using point counts to the use of autonomous recording units and citizen scientists data at two sites within the Mt Kenya ecosystem. We also present a new dataset of over 20 hours of recordings obtained from the Mt Kenya ecosystem and annotated by expert ornithologists. These audio recordings are used to demonstrate the use of large deep learning models to recognise species in the Mt Kenya ecosystem., , , # Ndege Zetu: A dataset to compare bird species monitoring approaches in the Mt Kenya ecosystem

https://doi.org/10.5061/dryad.d51c5b0c7

Description of the data and file structure

We present a dataset used to compare traditional bird survey approaches using point counts to the use of autonomous recording units and citizen scientists data at two sites within the Mt Kenya ecosystem.The dataset contains over 20 hours of new recordings obtained from the Mt Kenya ecosystem and annotated by expert ornithologists. The two sites are the Dedan Kimathi University Wildlife Conservancy (DeKUWC) and the Mt Kenya National Park (MKNP).

Files and variables

File: ndege-zetu.zip

Description:

This file contains three folders namely:

• annotations
• audio
• embeddings

The files in these directories are as shown below

├── annotations
│  ├── dekuwc-aru-2016.csv
│  ├── dekuwc-aru-2017.csv
│  ├── dekuwc-kbm.csv
│  ├── dekuwc-pc-2017.cs...