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425 results for “Washington”

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edi60/100

Cone production of upper slope conifers in the Cascade Range of Oregon and Washington, 1959 to 2022

Seed supply is a key feature of tree population dynamics, and seed production may be indicative of environmental and biological drivers. This study examines cone production in upper-slope, true fir-hemlock forests of the Pacific Northwest, starting in 1959 to the present. Annual surveys of cone counts of Abies spp. (A. amabilis, A. concolor, A. grandis, A. lasiocarpa, A. magnifica, A. procera), Pinus spp. (P. engelmannii, P. lamberti, P. monticola), and Tsuga spp. (T mertsiana) have been conducted at sixty-one plots in 37 locations in nine national forests in Washington and Oregon (originally 10 national forests, but Mt. Baker and Snoqualimie were combined). At each site, a visual count is made of cone production in each of a number (20-30) trees in a stand of one tree species. At some plots, additional trees were added in the 1980s. Primary data include numbers of counts per tree per year, periodic measurements of tree diameter, and the names of the sites. These data illustrate the periodicity of cone production cycles, as well as longer trends associated with climate change and variability in the region.

openCC (other)Mar 2023View details →

edi60/100

Demonstration of Ecosystem Management Options (DEMO) Study, western Oregon and Washington (post-treatment data, 1998-2016)

The Demonstration of Ecosystem Management Options (DEMO) Study is a regional-scale experiment in variable-retention harvest, established at six sites in western Oregon and Washington. Initiated in 1994, DEMO was designed to assess newly established standards and guidelines for regeneration harvests in mature, coniferous forests of the Pacific Northwest. The experiment is a randomized complete block design. It includes six treatments that represent strong contrasts in the level of retention (15-100% of original basal area) and the spatial pattern in which trees are retained (uniformly dispersed vs. aggregated in 1-ha patches). The factorial nature of the design (15 and 40% retention in both an aggregated and dispersed pattern) is unique among variable-retention experiments, regionally and globally. Long-term measurements of vegetation response lie at the core the DEMO Study. Key response variables include overstory tree growth and mortality, the dynamics of snags, regeneration of conifers (including planted seedlings and natural recruitment), and the composition, structure and diversity of the understory (including herbaceous, woody, and bryophyte species). Pre-treatment measurements were made between 1994 and 1996 (data are archived under Study Code TP104). Post-treatments measurements have occurred at ~5- to 7-year intervals between 1998 and 2016 (data are archived under Study Code TP108).

openCC (other)Jun 2023View details →

edi60/100

Lake Sediment Pollen from Benson Pond in Washington MA from 13834 BP to Present

Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C years before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as b

openCC0Dec 2023View details →

edi60/100

Lake Sediment Pollen from Guilder Pond in Mount Washington MA from 13882 BP to Present

openCC0Dec 2023View details →

zenodo48/100

Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.

<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4>There are two datasets in this data release:1. Model training dataset. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. 2. Model output dataset. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (Ritchie et al., 2018). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following Over et al., 2021) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):1. vegetation / other (0)2. water (1)3. sediment (2)4. large wood (3)<h3>1. Model training dataset.</h3>Imagery was labeled using a combination of the open-source software Doodler (Buscombe et al., 2021; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (Buscombe and Goldstein, 2022). Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.<h4>Files:</h4>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs2. Model_training_ images1of4.zip, 1.5 GB, imagery tiffs3. Model_training_ images2of4.zip, 1.5 GB, imagery tiffs4. Model_training_ images3of4.zip, 1.7 GB, imagery tiffs5. Model_training_ images4of4.zip, 1.6 GB, imagery tiffs<h3>2. Model output dataset.</h3>Imagery was labeled using a deep-learning based semantic segmentation model (Buscombe, 2023) trained specifically for the task using the Segmentation Gym (Buscombe and Goldstein, 2022) modeling suite. We use the software package Segmentation Gym (Buscombe and Goldstein, 2022) to fine-tune a Segformer (Xie et al., 2021) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of Xie et al. (2021), itself fine-tuned on ADE20k dataset (Zhou et al., 2019) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. <h4>Files:</h4>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)2. ElwhaMR_ imagery_ part1_ of_ 2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)3. ElwhaMR_ imagery_ part2_ of_ 2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)4. ElwhaLR_ imagery_ part1_ of_ 2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)5. ElwhaLR_ imagery_ part2_ of_ 2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)  This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. <h3>References</h3>• Buscombe, D. (2023). Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021). Human-in-the-loop segmentation of Earth surface imagery. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>• Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, pp.12077-12090.• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127, pp.302-321.

opencc-by-4.0Nov 2023View details →

zenodo48/100

Audio tagging of avian dawn chorus recordings in California, Oregon, and Washington

General Summary This acoustic data collection includes 1,575 5-minute soundscape recordings randomly selected from passive acoustic recordings made at 525 sites during 2022 on federally managed lands in western California, Oregon, and Washington, USA. We fully labeled 141 recordings (11.75 hrs) with 39,717 annotations for 118 sound types, including 58 avian species, two mammalian species, six aggregated biotic sounds, and eight non-biotic sound types. An additional 215 recordings were partially annotated with 1,466 annotations. The remaining unlabeled recordings have been included to facilitate novel research applications and methodological evaluations. Beyond the labeled soundscape recordings, we have included township and range identifications and 38 environmental covariates for each recording location. Data Collection Lesmeister et al. (2021) collected passive acoustic recordings during 2022 in support of long-term monitoring of federally threatened northern spotted owl (Strix occidentalis caurina) populations under the Northwest Forest Plan Effective Monitoring Program (U. S. Fish and Wildlife Service 1990, U. S. Department of Agriculture and U. S. Department of the Interior 1994). These data were collected at 643 hexagons that were randomly selected from a tessellation of 5 km2 hexagons covering the entire range of the northern spotted owl (Northern California, Oregon, Washington) under a selective constraint that hexagons contain ≥ 50 % forest-capable lands (def. forested lands or lands capable of developing closed-canopy forests) and be ≥ 25% federal ownership (Davis et al., 2011). Each hexagon was sampled by four Song Meter 4 (SM4) acoustic recording units (Wildlife Acoustics, Maynard, MA) deployed in a standardized spatial arrangement, such that recorders on a site were placed ≥ 500 m apart and were ≥ 200 m from the edge of the sampling hexagon boundary. Recorders were mounted to small trees (15 – 20 cm diameter at breast height) approximately 1.5 m above the ground and were placed on mid-to-upper slopes and ≥ 50 m from roads, trails, and streams. The SM4 devices each have two built-in omnidirectional microphones with a signal-to-noise ratio of 80 dB, typical at 1 kHz, and a recording bandwidth of 20 Hz – 48 kHz. Each device recorded ~11 hours of audio daily for six weeks from March to August at a sampling rate of 32 kHz. The daily recording schedule included a 4-hour window from two hours before sunrise to two hours after sunrise, a 4-hour window from one hour before sunset to 3 hours after sunset, and 10-minute recordings outside the two longer recording blocks at the start of every hour. Data Sampling The goal of this project was to develop a tagged audio dataset (hereafter project dataset) focused on the avian dawn chorus, which is an ecologically important period for the study of avian behavior (McNamara et al. 1987, Staicer et al. 1996, Zhang et al. 2015) and monitoring avian biodiversity (Bibby et al. 2000), but remains a challenging problem for acoustic classification systems (Duan et al. 2013, Stowell 2022). Passive acoustic monitoring on our sites occurs throughout the day. We filtered the full dataset to recordings collected between May and August during the hour immediately after sunrise. From the recordings meeting our filtering criteria, we randomly selected three 5-minute files from each site, which were assigned ordinal labels 'A, 'B,' or 'C.' The final project dataset comprised 131.25 hours of acoustic data. Annotation Protocol We randomly selected 141 sites from the project dataset and fully annotated each recording at a 2-second resolution. We applied labels to each 2-second window of the selected recordings following a predefined sound phonology library (available in the 'metadata.tsv' file), which concatenated the 2021 eBird taxonomy codes (Clements list; Clements et al. 2022) with standardized sonotype codes that incremented depending on the species repertoire (i.e., 'call_1,' 'song_1,' 'drum_1'). For example, 'herthr_song_1' is the label for Hermit Thrush, song_1. Unknown signals were labeled 'unknown,' and clips with no biotic signals (or noise classes of interest documented in metadata.tsv) were labeled 'empty.' Windows were labeled 'complete' and considered fully annotated when every signal was assigned an annotation. Files were deemed fully annotated when every 2-second window contained the 'complete' label. Environmental Covariates Sampling locations will not be published to afford protections for Federally Threatened or Endangered species which may occur on our sites. However, we provide the State, Township, and Range for each sampling location along with the site-specific values for 38 forest structure, topographic, and climatic environmental covariates developed by the Landscape Ecology, Modeling, Mapping, and Analysis group in the Pacific Northwest (<a href="https://lemma.forestry.oregonstate.edu/data">https://lemma.forestry.oregonstate.edu/data</a>; Ohmann and Gregory 2002). State, Township, and Range values are sufficient to explore geographic variation in species- or community-specific call and song phenology and the extracted environmental covariates may provide useful contextual information for novel machine-learning developments (Liu et al. 2018).  Description of Data Format The fully annotated audio files can be accessed by downloading and extracting "annotated_recordings.zip." Partially annotated and non-annotated audio files can be accessed by downloading and extracting "additional_recordings_part_1.zip" or "additional_recordings_part_2.zip." Acoustic file names contain site and replicate indicators, such that file "Site_001_Rep_A.wav' was recorded on site 1 and is the A replicate random draw from the available set of dawn chorus recordings. The site and replicate numbers link to additional recording information in "files.tsv," annotations in "annotations.tsv" and "partial_annotations.tsv," as well as site and replicate specific environmental characteristics in "environmental_characteristics.tsv." Metadata describing sound classes and environmental characteristics can be found in "metadata.tsv," and "environmental_characteristics_metadata.tsv." Acknowledgments Acoustic data collection was funded and collected by the US Forest Service and the US Bureau of Land Management. Annotation work was funded by Google. We would also like to thank the many biologists that collected and processed the data compiled here. The use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Government of any product or service.

opencc-by-4.0Feb 2023View details →

edi48/100

Washington Motus Prioritization Tool Data

Addressing survival and movement of priority migratory avian species of concern along the Pacific Flyway is paramount for their conservation. Yet, the migratory life stage is understudied in many avian species. The Motus radiotelemetry receiver network is an established system for tracking survival and movement of avian species. This network is an international collaborative that successfully identifies stopover site duration, connected migratory routes, post-fledging dispersal and survival, and adult survival and fidelity on a landscape-scale; parameters that cannot be easily estimated using non-tagged birds. While the Motus network is highly connected in eastern North America, the western part of the continent is lagging in coverage and connectivity, limiting the ability to obtain sample sizes large enough to robustly model demographic parameters from tagged birds. Thus, the expansion of the Motus network is a high priority for Pacific Flyway State Agencies. To date, no method exists for determining priority locations for new Motus receiving stations. With collaborations from States and the Canadian Province of British Columbia, we used eBird citizen scientist data to prioritize strategic locations for new Motus receiving stations throughout the Pacific Flyway. We model priority species’ co-occupancy of varying abundance states (i.e., absent, present, abundant, abundant in multiple weeks) with spatially varying Landsat (red and near infrared), water, land cover types, and weather covariates while accounting for variable detection with temporally varying survey effort covariates. Using occupancy model predictions, we identify high-use areas of the Pacific Flyway for establishing new Motus receiving towers that have high probabilities of intercepting high presence and /or abundance of multiple species of interest in a series of predictive occupancy maps. This package contains all the necessary files to recreate the data analysis, print out maps based on predictions

openCC0Feb 2025View details →

zenodo44/100

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

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.  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. 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 (e.g., 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 et al. 2019.

opencc-zeroNov 2019View details →

zenodo44/100

Line-level Named Entity Recognition annotation for the George Washington and IAM datasets

Line-level Named Entity annotation for the George Washington and IAM datasets. The word-level annotations from Oliver Tüselmann [3] were extended to line-level to enable experimentation with line-level coupled HTR+NER models. We also publish the line-level partition files that result from the partition proposed by [3].

opencc-by-4.0Apr 2023View details →

edi44/100

Dataset for Disturbance: a double-edged sword for restoration in a changing climate: Western Oregon and Washington upland prairies 2019-2020

Data associated with the paper submitted to Restoration Ecology in November 2021: Disturbance: a double-edged sword for restoration in a changing climate Alejandro Brambila1, Paul B. Reed1, Scott D. Bridgham1, Bitty A. Roy1, Bart R. Johnson2, Laurel Pfeifer-Meister1 and Lauren M. Hallett1 1. Institute of Ecology and Evolution, University of Oregon 2. Department of Landscape Architecture, University of Oregon In this project, we used this data to test how fire disturbance, designed to enhance restoration seeding success, combines with climate and initial vegetation conditions to shift perennial versus annual grass dominance and overall community diversity in Pacific Northwest grasslands. We seeded both native and introduced perennial grasses and native forbs in paired, replicated burned-unburned plots in three sites along a latitudinal climate gradient from southern Oregon to Washington. Past restoration and climate manipulations at each site had increased the variation of starting conditions between plots. This data is to be used with the script, full_disturbance_script.R, which can be accessed at https://github.com/HallettLab/hops. Includes the tables: plotkey.csv spkey.csv mixkey.csv vegplot.csv vegplot2020.csv

openCC0Nov 2021View details →

edi44/100

Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]

(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.

openCC0Apr 2022View details →

zenodo40/100

Block-level & block group-level income projections for Washington state under different SSPs from 2020 to 2100

The files "bg_binned_income_proj.csv" and "bk_binned_income_proj.csv" contain income projections in 2015 dollars for Washington state (under census geographic boundary 2020) from 2020 to 2100 at the block group and block level, respectively, based on different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5). The income projections are represented by the projected number of households for each of the three different income bins.In the files, GISJOIN is the unique identifier for each block (or block group) . Each number of households projection is stored in a column, where the first four characters of the column name represent the projection year (e.g., 2020, 2030), the following four characters represent the SSP (e.g., SSP2, SSP3, SSP5), and the remaining characters indicate the income bin (Income1 represents annual household income less than $18,150, Income2 represents annual household income between $18,150 and $48,396, and Income3 represents annual household income greater than $48,396). For example, "2020SSP2Income1" indicates the household number projection for the first income bin in 2020 under SSP2."README.txt" describes the general steps for income data generation.

opencc-zeroApr 2023View details →

zenodo40/100

Text-fig. 1. Map of the early Eocene Okanagan Highlands British Columbia, Canada and Washington, United States of America. Modified from Archibald et al. 2014: fig. 2, courtesy of Bruce Archibald. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance

Text-fig. 1. Map of the early Eocene Okanagan Highlands British Columbia, Canada and Washington, United States of America. Modified from Archibald et al. 2014: fig. 2, courtesy of Bruce Archibald.

opencc-by-4.0Dec 2023View details →

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Data from: European green crab predation in a Washington State estuary revealed with DNA metabarcoding

Fisher, MC, Grason, EW, Stote, A, Kelly, RP, Litle, K, & PS McDonald. (in review). Invasive European green crab (Carcinus maenas) predation in a Washington State estuary revealed with DNA metabarcoding.  Sequencing data files produced for Fisher et al. (in review) on an Illumina MiSeq (2x300bp) targeting a 418bp DNA sequence in the mitochondrial cytochrome C oxidase subunit I gene (cox1 or COI) Folmer region. Data are demultiplexed but otherwise un-processed. Sample data sheets used to load each sequencing run are included as excel spreadsheets in the appropriate zipped folder. The metadata file contains sample (green crab stomach / mock community) metadata and corresponding MiSeq run number(s).  Associated lab protocols are available on Github (<a href="https://github.com/mfisher5/Green-crab-dDNA/tree/main/doc" target="_blank" rel="noopener">Green-Crab-dDNA</a>) --- Abstract: Predation by invasive species can threaten local ecosystems and economies. The European green crab (Carcinus maenas), one of the most widespread marine invasive species, is an effective predator associated with clam and crab population declines outside of its native range. In the U.S. Pacific Northwest, green crab has recently increased in abundance and expanded its distribution, generating concern for estuarine ecosystems and associated aquaculture production. However, regionally-specific information on the trophic impacts of invasive green crab is very limited. We compared the stomach contents of green crabs collected on shellfish aquaculture beds versus natural intertidal sloughs in Willapa Bay, Washington, to provide the first in-depth description of European green crab diet at a particularly crucial time for regional management. We first identified putative prey items using DNA metabarcoding of stomach content samples. We compared diet composition across sites using prey presence/absence and an index of species-specific relative abundance. For eight prey species, we also calibrated metabarcoding data to quantitatively compare DNA abundance between prey items, and to describe an ‘average’ green crab diet at an intertidal slough and an actively cultivated Manila clam bed. From the stomach contents of 61 green crabs, we identified 54 unique taxa belonging to nine phyla. The stomach contents of crabs collected from cultivated Manila clam beds were significantly different from the stomach contents of crabs collected at natural intertidal sloughs. Across all sites, arthropods were the most frequently detected prey, with the native hairy shore crab (Hemigrapsus oregonensis) the single most common prey item. Of the eight species included in the quantitative model, two ecologically-important native species – the sand shrimp (Crangon franciscorum) and the Pacific staghorn sculpin (Leptocottus armatus) – were the most abundant in crab stomach contents, when present. In addition to providing timely information on green crab diet, our research demonstrates the novel application of a recently developed model for more quantitative DNA metabarcoding. This represents another step in the ongoing evolution of DNA-based diet analysis towards producing the quantitative data necessary for modeling invasive species impacts.

opencc-by-4.0Mar 2024View details →

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Github Repository for: European green crab predation in a Washington State estuary revealed with DNA metabarcoding

Fisher, MC, Grason, EW, Stote, A, Kelly, RP, Litle, K, & PS McDonald. (2024). Invasive European green crab (Carcinus maenas) predation in a Washington State estuary revealed with DNA metabarcoding. DOI:10.1371/journal.pone.0302518 Github release v1.1 of the repository for Fisher et al. 2024, "European green crab predation in a Washington State estuary revealed with DNA metabarcoding." For the most updated repository, see: <a href="https://github.com/mfisher5/Green-crab-dDNA/tree/main/doc">github.com/mfisher5/Green-crab-dDNA</a> Contains the code and minimum dataset necessary to replicate study findings.   --- Abstract: Predation by invasive species can threaten local ecosystems and economies. The European green crab (Carcinus maenas), one of the most widespread marine invasive species, is an effective predator associated with clam and crab population declines outside of its native range. In the U.S. Pacific Northwest, green crab has recently increased in abundance and expanded its distribution, generating concern for estuarine ecosystems and associated aquaculture production. However, regionally-specific information on the trophic impacts of invasive green crab is very limited. We compared the stomach contents of green crabs collected on shellfish aquaculture beds versus natural intertidal sloughs in Willapa Bay, Washington, to provide the first in-depth description of European green crab diet at a particularly crucial time for regional management. We first identified putative prey items using DNA metabarcoding of stomach content samples. We compared diet composition across sites using prey presence/absence and an index of species-specific relative abundance. For eight prey species, we also calibrated metabarcoding data to quantitatively compare DNA abundance between prey items, and to describe an ‘average’ green crab diet at an intertidal slough and an actively cultivated Manila clam bed. From the stomach contents of 61 green crabs, we identified 54 unique taxa belonging to nine phyla. The stomach contents of crabs collected from cultivated Manila clam beds were significantly different from the stomach contents of crabs collected at natural intertidal sloughs. Across all sites, arthropods were the most frequently detected prey, with the native hairy shore crab (Hemigrapsus oregonensis) the single most common prey item. Of the eight species included in the quantitative model, two ecologically-important native species – the sand shrimp (Crangon franciscorum) and the Pacific staghorn sculpin (Leptocottus armatus) – were the most abundant in crab stomach contents, when present. In addition to providing timely information on green crab diet, our research demonstrates the novel application of a recently developed model for more quantitative DNA metabarcoding. This represents another step in the ongoing evolution of DNA-based diet analysis towards producing the quantitative data necessary for modeling invasive species impacts.

opencc-by-4.0Apr 2024View details →

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ARCOS Database provided by the Washington Post

ARCOS Database provided by the Washington Post. We use the data for the empirical analysis in our article`` Retail Pharmacies and Drug Diversion during the Opioid Epidemic’’. 2006--2012 data from the Automation of Reports and Consolidated Orders System (ARCOS), maintained by the Diversion Control Division of the US Drug Enforcement Administration (DEA).   The data can be downloaded from https://www.washingtonpost.com/national/2019/07/18/how- download-use-dea-pain-pills-database/ in raw format and until 2021 through an R package (API) on https://github.com/wpinvestigative/arcos. Please follow the requirement of the Washington Post: ‘If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project.” (The Washington Post, 2019)   The Washington Post. How to download and use the DEA pain pills database, 2019. https://www.washingtonpost.com/national/2019/07/18/how-download-use-dea-pain-pills-database/

opencc-by-4.0May 2022View details →

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Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015). in First Water Lily, A Leaf Of Nymphaea Sp., From The Miocene Clarkia Flora, Northern Idaho, Usa: Occurrence, Taphonomic Observations, Floristic Implications

Text-fig. 1. Locality map with the approximate extent of Clarkia Lake during Miocene times in what is today northern Idaho, USA. Black dots mark three of the localities yielding the Miocene Clarkia flora; the fossil leaf of Nymphaea sp. described here comes from locality P-33. Other symbols: Dashed lines for county boundaries; a thin dotted line for Idaho State Hwy 3; a triangle for the local peak of Bechtel Butte; and a star for the town of Clarkia. Inset: Location of the map in northern Idaho. Abbreviations: WA – Washington state, OR – Oregon, ID – Idaho, MT – Montana. Map redrawn from Ladderud et al. (2015).

opencc-by-4.0Aug 2022View details →

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Dataset: Washington Trust Bancorp, Inc. (WASH) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

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FIGURE 3 in A new early occurrence of Cervidae in North America from the Miocene-Pliocene Ellensburg Formation in Washington, USA

FIGURE 3. Generalized stratigraphic sequence of Craig's Hill locality, showing approximate positions of dates from Lamb (1997). Abbreviations: DT, dated tephra; F, overbank deposit with fossil.

opencc-by-4.0Dec 2020View details →

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FIGURE 4 in A new early occurrence of Cervidae in North America from the Miocene-Pliocene Ellensburg Formation in Washington, USA

FIGURE 4. Specimen UWBM VP 117138, cf. Bretzia, right mandible fragment with m2 and m3 in occlusal (A), lingual (B), and labial (C) views, and associated p2 in occlusal (D) and labial (E) views. Dental nomenclature (F) follows Gustafson (2015). Scale bar equals 1 cm.

opencc-by-4.0Dec 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record