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709 results for “NC”
NRCS-USFS Soil Moisture Measurements - Coweeta Hydrologic Laboratory, NC, 2022-2025
This dataset consists of soil moisture (volumetric water content and water potential), temperature, and electrical conductivity measurements at multiple depths within 12 soil pedons distributed across Watersheds 32 and 7 at the Coweeta Hydrologic Laboratory from March 2022 to April 2025. This work is a part of a larger partnership between the U.S. Forest Service (USFS) and the Natural Resources Conservation Service (NRCS) to install, monitor and generate long-term soil moisture datasets across multiple forested watersheds in the U.S. Associated data packages from both the Fernow and Hubbard Brook Experimental Forests can be found on the EDI Data Portal. Dataset contributors: Project planning led by Carlos Quintero (USFS, ORISE), with help from Amos Stead (NRCS) and Tiffany Allen (NRCS) in site selection. Scientific and logistical support from Chris Oishi (USFS), Amanda Pennino (NRCS), and Erin Rooney (NRCS). Seth Strickland (USFS), Amos Stead (NRCS), Ann Tan (NRCS), and Tiffany Allen (NRCS) assisted with site installation. Site visits, data downloading, and logger maintenance was by Seth Strickland (USFS). The dataset was curated by Emily Piché (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS)
Indicators of Contaminant Sources, PFAS, and Water Quality in Ellerbe Creek and New Hope Creek, NC (2019-2022)
Thousands of chemical contaminants are found in urban stream globally. This is a dataset of water quality measures of (1) compounds that are indicative of specific contaminant sources, (2) common water quality measures [trace metals, major ions, nutrients], and (3) PFAS. Sampling was conducted in Ellerbe Creek and New Hope Creek in the Durham and Orange counties of North Carolina. Biweekly and synoptic sampling was undertaken to explore spatial and temporal variation in water concentrations.
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 01, Highlands Biological Station, Highlands, NC, USA, 2022-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Betula alleghanensis and formerly Tsuga canadensis, the latter of which has mostly succombed to the Hemlock Woolly Adelgid.
Daily Summary of Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 01, Highlands Biological Station, Highlands, NC
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Betula alleghanensis and formerly Tsuga canadensis, the latter of which has mostly succombed to the Hemlock Woolly Adelgid.
Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 02, Highlands Biological Station, Highlands, NC, 2021-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in a remnant old-growth Canada Hemlock Forest (typic subtype) community dominated by an understory of Rhododendron maximum and an overstory of Tsuga canadensis, the majority of which are still alive and have been treated with systemic insecticides to protect against infestations of the Hemlock Woolly Adelgid. Other trees include Betula alleghanensis, Acer rubrum, and Quercus rubra. The pressure transducer is located in the thalweg of Coker Creek, a second order stream that flows into Lindenwood Lake.
Daily Summary of Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 02, Highlands Biological Station, Highlands, NC, 2021-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in a remnant old-growth Canada Hemlock Forest (typic subtype) community dominated by an understory of Rhododendron maximum and an overstory of Tsuga canadensis, the majority of which are still alive and have been treated with systemic insecticides to protect against infestations of the Hemlock Woolly Adelgid. Other trees include Betula alleghanensis, Acer rubrum, and Quercus rubra. The pressure transducer is located in the thalweg of Coker Creek, a second order stream that flows into Lindenwood Lake.
Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 03, Highlands Biological Station, Highlands, NC, 2021-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Liriodendron tulipifera, Betula alleghanensis, and Tsuga canadensis, the latter of which has several trees that have succombed to the Hemlock Woolly Adelgid, though living trees have been treated with a systemic insecticide. The pressure transducer is located in a second order stream known as Station Branch.
Daily Summary of Soil moisture, soil temperature, air temperature, stream water temperature, stream stage and discharge data from Soil Moisture Station 03, Highlands Biological Station, Highlands, NC, 2021-2025
Measurements of soil moisture, soil temperature, air temperature, stream temperatue, and stream stage/discharge were collected as part of a long-term monitoring project at the Highlands Biological Station, Western Carolina University, Highlands, North Carolina. The sensor station is located in an acidic cove forest (high elevation subtype) dominated by an understory of Rhododendron maximum and an overstory of Liriodendron tulipifera, Betula alleghanensis, and Tsuga canadensis, the latter of which has several trees that have succombed to the Hemlock Woolly Adelgid, though living trees have been treated with a systemic insecticide. The pressure transducer is located in a second order stream known as Station Branch.
Fish and crayfish density and count data for Peeks Creek, Macon County, NC, USA 2005-2014, 2019, and 2022 following a catastrophic debris flow, as well as six reference streams
We followed the process of recovery of the fish and crayfish assemblage in Peeks Creek, a high-gradient second order stream in the Little Tennessee River watershed of North Carolina, after a debris flow devastated the channel and its riparian zone. After 15 years, the fish assemblage had recovered, and the channel and riparian zone had stabilized. Of the three major components of the fish assemblage, Rainbow Trout (Oncorhynchus mykiss (Walbaum)), a strong swimmer, reappeared in year 1. Longnose Dace (Rhinichthys cataractae (Valenciennes in Cuvier and Valenciennes)) reappeared in year 3. Mottled Sculpin (Cottus bairdii Girard), a weak swimmer, did not become established until year 6 and only resumed expected abundance in year 9. Appalachian Brook Crayfish (Cambarus bartonii cavatus Hay) numbers recovered quickly, though only one individual was found the year following the debris flow. Unassisted natural recovery occurred after a costly engineered restoration project had been rejected and arguably represents the preferable solution. However, recovery of the fish assemblage may not have been achieved if the stream flowed directly into an impoundment or low gradient river that lacked the source of species for recolonization, or if the stream had been located above a barrier to upstream movement.
Measurements of Coarse Woody Debris %C and %N at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Coarse Woody Debris Cations Measurements at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
NC Community College President Data Set
<p>NCCCPDS. A working data set of presidents serving in North Carolina community colleges from the mid-1960s. Includes name, year, college, gender identity, degree, degree university, and field. Data were retrieved from publicly available documents including course catalogs, newspapers, obituaries, and university alumni records.</p>
CINECA synthetic cohort Europe CH SIB [CC-BY-NC-SA]
<p>The “CINECA synthetic cohort EUROPE CH SIB” dataset consists of 6,733 synthetic samples with phenotypic and genotypic information. The synthetic phenotypic data were created from the CoLaus and PsyColaus cohort and the synthetic genetic data from the 1000 Genomes project. This dataset was created within the context of the CINECA project. More information about the creation of this dataset can be found in the included documentation. The phenotypic synthetic data is available under the Creative Commons Attribution (https://creativecommons.org/licenses/by/4.0/) and the genotypic data is available under the Creative Commons Attribution Non-Commercial Share-Alike license.To view a copy of the license, please visit: <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>.</p> <p> </p>
CINECA synthetic cohort NA Canada CHILD [CC-BY-NC-SA]
<p>The "CINECA synthetic cohort NA Canada CHILD" dataset is a synthetic dataset developed to provide insight into how data is structured for select common attributes in the <a href="https://childstudy.ca/">CHILD Cohort Study</a>, but not reveal any personal or identifiable information associated with cohort participants. Such synthetic datasets are valuable for software developers to be able to see specific examples of data for common attributes (i.e. a minimal metadata model of a selection of common variables usually present in cohorts). This dataset comprises 100 variables for 150 synthetic participants which have faked phenotypic data that reflects CHILD cohort data. In addition, there is genetic data based on the <a href="https://www.nature.com/articles/nature15393">1000 Genomes</a> project. This dataset was created within the context of the <a href="https://www.cineca-project.eu/">CINECA</a> project. More information about the creation of this dataset can be found in the included documentation. </p> <p><br> <em>Please note this preamble must be included with any distribution of this dataset: </em>This synthetic dataset (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about CHILD cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY; <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>). Please ensure this preamble is included with this dataset and that the CHILD project and the CINECA project (funding: EC H2020 grant 825775 and CIHR grant 404896) are acknowledged. If you have any questions about this dataset contact Fiona Brinkman at brinkman@sfu.ca or Erin Gill at egill@sfu.ca.</p> <p> </p> <p><strong>CINECA synthetic cohorts</strong></p> <ul> <li><a href="https://zenodo.org/record/4955933">CINECA synthetic cohort Africa H3ABioNet</a></li> <li><a href="https://zenodo.org/record/5082689">CINECA synthetic cohort Europe CH SIB</a></li> <li><a href="https://ega-archive.org/datasets/EGAD00001006673">CINECA synthetic cohort Europe UK1</a></li> </ul> <p> </p>
Diachronic Corpus of Mission Statements for NC and FL Community Colleges
<p>This is a diachronic corpus of mission statements, philosophy statements, and purpose statements for community colleges in North Carolina and Florida. Texts date from the mid-1960s to 2020. Texts are indexed to IPEDS unit id. Texts for some years are missing. "OTM" means other than mission (which is typically a statement of purpose but may include statement of goals). Data were retrieved from archived catalogs and archived websites (e.g., Wayback Machine). The highest level of heading was used. For example, if a college published a statement of mission and a statement of purpose, the statement with the most prominent (typically the first) heading was used. </p>
Detecting coarse beach sediment using remotely sensed imagery at the FRF, Duck, NC, USA: Labeled images, deep learning model, testing data, and predictions.
<p>This data record contains 5 zip files all used to build and use a semantic segmentation model to operate on beach imagery taken at the Field Research Facility (FRF) in Duck, North Carolina, USA. All data is from 2015-2021</p> <p>The `training_data.zip` contains all data used to train the ML model. All images come from the north facing (c1) camera. This zip file includes: a list of classes used to label the imagery, and folders of 107 images, 107 sparse annotations (doodles), 107 labels, and 107 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021).</p> <p>The `model.zip` file contains the ML model, and associated metadata. This includes: a JSON model configuration file, a figure showing model training statistics, an `.npz` file of model training output, a list of training and validation files, the model as an h5 file and in the Tensorflow ‘saved model’ format. All modeling was done with Segmentation Gym (Buscombe & Goldstein 2022).</p> <p>The `test_data_c6.zip` file contains all data from the south facing (c6) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with the open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `test_data_c1.zip` file contains all data from the north facing (c1) camera to test the ML model. This includes: a list of classes used to label the imagery, and folders of 10 images, 10 sparse annotations (doodles), 10 labels, and 10 overlays. All labeling was done with an open-source labeling tool ‘Doodler (Buscombe et al., 2021). Testing the model with this data was done with codes in: https://github.com/ebgoldstein/FRF_GrainSize</p> <p>The `predictions.zip` file contains 4418 images from the north facing (c1) camera that were run through the trained segmentation model as well as the resulting output (presented as side-by-side image and overlays). These images were created using codes in Segmentation Gym (Buscombe & Goldstein 2022).</p>
Aquatic Insect Adult Metals Dataset: Urban and Forested Watersheds in the Piedmont of NC - 2021-2022
This dataset reports concentrations of 6 target trace metals (copper, zinc, nickel, lead, chromium, and selenium) in unfiltered water, emergent aquatic adult insects (by family), biofilm mats (predominately algae), and tree roots submerged under stream water. Biological and water samples were collected from three streams in the Piedmont region of North Carolina, USA: a wastewater dominated site (Ellerbe Creek, near the USGS gage at Glen Road ), a stormwater dominated site (Ellerbe Creek, near the USGS gage on Club Blvd), and a stream draining a predominately forested watershed (New Hope Creek, near a StreamPULSE site at Hollow Rock Preserve). This data was submitted for publication in a manuscript that explores how metals are transported by aquatic emergent insects from stream ecosystems into terrestrial food webs.
Macon County, NC Middle School Climate Data
The climate station at the Macon Middle School near Franklin, NC was established in February 2000. The objectives were to: 1) locate a climate station outside of the Coweeta Basin, but within Macon County, North Carolina, measuring air temperature, air humidity, precipitation, solar radiation, wind speed, wind direction, vapor pressure, and barometric pressure 2) provide students and teachers at the school with a unique teaching tool, and 3) provide the public with an online archive of weather data.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.