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3,846 results for “2023”
GRIME AI Water Segmentation Model for the USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for USGS Monitoring Site at East River at County Trunk HWY ZZ near Greenleaf, WI. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/WI_East_River_at_HWY_ZZ_near_Greenleaf for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated duri
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Rio Grande below Elephant Butte Dam, NM, 2023-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NM_Rio_Grande_below_Elephant_Butte_Dam for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated du
Interviews with members of the HJA Community during the Lookout Fire, HJ Andrews Experimental Forest, 2023
This dataset records the interview instrument, analytical codebook, and summary of results for the 2023 Lookout Fire Qualitative Interviews. Data was collected in 2023 in Corvallis, Oregon, and over Zoom. Members of the H. J. Andrews Experimental Forest (HJA) community (e.g., university faculty and administrative professionals, agency scientists and personnel, students, alumni and emeritus from the aforementioned communities) were interviewed between September 26th and November 8th 2023. At the time, the fire had largely stopped growing (no significant runs occurred during the interview period), but the fire was not fully contained and the fire severity was not yet known by the community. Data collection is complete. The interview included questions about emotional reactions to the Lookout Fire, current and foreseen impacts to research at the HJA, social relationships and the fire, naturalness of the fire, and climate change, climate anxiety, and the fire. Interviews were semi-structured; while interviews were guided by the interview protocol, conversation was allowed to proceed organically. In total, 40 respondents were interviewed. Interviews were transcribed verbatim and analyzed inductively and deductively. A finalized codebook was developed iteratively; the included codebook are the final codes used to analyze the full dataset. Interview transcripts and other potentially identifying information is not available to protect respondent confidentiality and anonymity. This dataset summarizes the key interview results.
Ice, water, and sediment pigment concentrations from Beaufort Sea lagoons core program stations, 2023-24
Bottom ice (< 20 cm), water column, and undisturbed surface sediment samples from the Beaufort Lagoon Ecosystem Long Term Ecological Research programs were collected, in tandem, from core program sites in ice-cover (~April), ice break-up (~June), and open water (~August) seasons of 2023, and ice-cover 2024, to quantify algal pigment concentrations and variations in an annual cycle. We also ran historical samples from 2021 sampling seasons. This data can be used with analysis programs such as CHEMTAX or PhytoClass to elucidate microalgal community structure. Fourteen pigments were measured, including chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, prasinoxanthin, lutein, chlorophyll c<sub>3</sub>, 19-hexanoyloxyfucoxanthin, and 19-butanoyloxyfucoxanthin. Phaeopigments (pheophytin, pheophorbide, and chlorophyllide a) were also included in these analyses. For sediment samples, the values of chlorophyll a, fucoxanthin, zeaxanthin, alloxanthin, peridinin, pheophytin, pheophorbide, and chlorophyllide a can be found in the core program pigment dataset, which is a continuously collected data set (<a href="https://doi.org/10.6073/pasta/5294f45c9c7287903078926a487f1fd7" style="text-decoration: underline;">Sediment pigment concentrations</a>). Pigment concentrations were measured using high-precision liquid chromatography (HPLC). Concentrations are represented as μg L<sup>-1</sup> for both ice and water column samples, and as μg g<sup>-1</sup> for sediment samples.
Long-term composited land surface temperature for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023
This data package consists of multiple decades of land surface temperature (LST) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). We derived LST values based on the thermal band from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations: - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Fall 2023 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in August 2023. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
GCE-LTER Altamaha River Plant Community Monitoring Survey in October 2023
A quadrat survey was conducted in October 2023 to measure the species and size distribution of plants at 3 sampling sites on the creekbank of the Altamaha River. The sites were chosen to capture the transition from Spartina alterniflora to Spartina cynosuroides (site SCSA) and the transition from Spartina cynosuroides to Zizaniopsis miliacea (sites ZSC1 and ZSC2). The quadrats were established as permanent plots in October 2012 by placing PVC stakes along the creekbank at each site. Plots were evenly spaced, but were not randomly located because the goal was to start with mixtures of vegetation in most of the plots, and vegetation was distributed in patches along the creekbanks. Therefore, these plots provide useful measures of vegetation change, but are not a random sample of the vegetation at the site. Plots will be replaced each year as necessary to replace any lost to disturbance. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set. This survey will be repeated annually to assess changes in plant distribution and biomass in relation to environmental changes documented by other GCE LTER monitoring efforts.
NRCS-USFS Soil Moisture Measurements - Hubbard Brook Experimental Forest, 2023-2025
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 3, 6, and 9 at Hubbard Brook Experimental Forest from July 2023 to June 2025. This work is a part of the Forest Soil Moisture Monitoring Network (FSMMN), which is an interagency partnership between the U.S. Forest Service 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. Dataset contributors: Hubbard Brook site selection and project planning was conducted by Amanda Pennino (NRCS), Scott Bailey (Virginia Tech) and Mark Green (Case Western). Site visits, data downloading, and logger maintenance was by Lucy Zendzian (NRCS), Paul Gadecki (NRCS), and Jack Ferrara (NRCS). The dataset was curated by Emily Piche (USFS, ORISE) and Amanda Pennino (NRCS). Overall partnership initiation and project management was by Stephanie Connolly (USFS) and Skye Wills (NRCS).
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
GLBRC Aboveground Plant Biomass at the Kellogg Biological Station, Hickory Corners MI (2008 to 2023), and the Arlington Research Station,Arlington, WI (2008 to 2014)
Dataset Abstract Aboveground biomass of BCSE herbaceous perennial crop treatments (G4 starting in 2021, G5-G7, G9-G10). Peak biomass samples were sorted to species from 2009-2017 but are left “unsorted” from 2018 onward. original data source http://lter.kbs.msu.edu/datasets/82
Cascade Project at North Temperate Lakes LTER Core Data Carbon 1984 - 2023
Data on dissolved organic and inorganic carbon, particulate organic matter, partial pressure of CO2 and absorbance at 440nm. Samples were collected with a Van Dorn sampler. Organic carbon and absorbance samples were collected from the epilimnion, metalimnion, and hypolimnion. Inorganic samples were collected at depths corresponding to 100%, 50%, 25%, 10%, 5%, and 1% of surface irradiance, as well as one sample from the hypolimnion. Samples for the partial pressure of CO2 were collected from two meters above the lake surface (air) and just below the lake surface (water). Sampling frequency: varies; number of sites: 14
Mercury in soil, vegetation, and organisms across Niwot Ridge, Saddle Catchment, and Green Lakes Valley, 2020 - 2023.
This dataset includes soil, vegetation, water, atmospheric deposition, litterfall, incubation, and organism data from the Niwot Ridge, Saddle Catchment, and Green Lakes Valley collected during 2020 and 2021 to investigate the storage, transformation, and mobilization of mercury in the Colorado Rocky Mountains. During Summer 2020, we collected soil cores (10cm x 3cm) across vegetation plant functional groups in wet meadows, moist meadows, dry meadows, krummholz, subalpine forest, shrub areas, as well as at the inlet and outlet of the Green Lakes in Green Lakes Valley. At each of these sites, we collected leaves from forbs, graminoids, and shrubs, as well as litter (and moss if present). For organisms, we sampled pika hairs from nine different pika trapped on the West Knoll, in addition to caddisfly pupae found in wet meadows in the Saddle Catchment. We analyzed hairs from weasel specimens at the CU Boulder Natural History Museum that were trapped either on, or near, Niwot Ridge. Finally, we analyzed dust samples collected by Dr. Ruth Heindel in 2018 and 2019 on Niwot Ridge. We analyzed soil samples for organic matter; pH; water content; percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed vegetation samples for percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed organism and dust samples for total mercury and methylmercury. During Spring 2021, we collected composite snow cores from 4 sites in the Saddle region and 3 sites in the subalpine forest. We measured snow depth and density to calculate snow water equivalent and then analyzed these samples for sulfate, nitrate, chloride, dissolved organic carbon, dissolved organic nitrogen, total mercury, and methylmercury concentrations. During Summer 2021, we collected soil cores (10cm x 3cm) every other week from June through September from a solifluction lobe, alpine wet
Time-lapse camera (phenocam) imagery of black sand extended growing season length experiment, 2022 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and potentially earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes phenocam images from 2022-2023.
PIE LTER 10-minute marsh water table height at Shad Creek, Rowley, MA from May-November 2023.
Measurements of water table height in the Shad Creek marsh located near the Shad Creek eddy flux tower, Rowley, MA. Measurements were taken every 10 minutes at each logger along a transect of water level loggers running perpendicular to the Shad Creek stream bank at Shad Creek from May-November 2023.
Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</p>
Daten der Data Literacy Bedarfserhebung für die historisch arbeitenden Disziplinen (Erhebungszeitraum: August-Oktober 2023)
<p>Bei dieser Publikation handelt es sich um Daten aus der NFDI4Memory Data Literacy Bedarfserhebung, die von August bis Oktober 2023 durchgeführt wurde. Der Datensatz beinhaltet die Rohdaten, wie sie aus dem Fragebogentool SoSciSurvey heruntergeladen wurden und die aufbereiteten Daten, die die Grundlage für die Auswertung waren.</p> <p><strong>Rohdaten aus SoSciSurvey:</strong></p> <p>.xlsx-Format: </p> <ul> <li><a href="../api/records/12166939/draft/files/codebook_4memory-data-literacy_2024-06-19_11-28.xlsx/content" target="_blank" rel="noopener noreferrer">codebook_4memory-data-literacy_2024-06-19_11-28.xlsx</a></li> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-28.xlsx/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-28.xlsx</a></li> </ul> <p>.csv-Format:</p> <ul> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/variables_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">variables_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/rdata_4memory-data-literacy_2024-06-19_11-33.csv/content" target="_blank" rel="noopener">rdata_4memory-data-literacy_2024-06-19_11-33.csv</a></li> <li><a href="../api/records/12166939/draft/files/sdata_4memory-data-literacy_2024-06-19_11-32.csv/content" target="_blank" rel="noopener">sdata_4memory-data-literacy_2024-06-19_11-32.csv</a></li> <li><a href="../api/records/12166939/draft/files/values_4memory-data-literacy_2024-06-19_11-30.csv/content" target="_blank" rel="noopener noreferrer">values_4memory-data-literacy_2024-06-19_11-30.csv</a></li> <li><a href="../api/records/12166939/draft/files/Codebuch.csv/content" target="_blank" rel="noopener noreferrer">Codebuch.csv</a></li> <li><a href="../api/records/12166939/draft/files/Ausgangsdatensatz.csv/content" target="_blank" rel="noopener noreferrer">Ausgangsdatensatz.csv</a></li> </ul> <p>.sql-Format:</p> <ul> <li><a href="../api/records/12166939/draft/files/data_4memory-data-literacy_2024-06-19_11-33.sql/content" target="_blank" rel="noopener noreferrer">data_4memory-data-literacy_2024-06-19_11-33.sql</a></li> </ul> <p>.sps-Fromat:</p> <ul> <li><span><a href="../api/records/12200702/draft/files/spss_4memory-data-literacy_2024-06-19_11-31.sps/content" target="_blank" rel="noopener noreferrer">spss_4memory-data-literacy_2024-06-19_11-31.sps</a></span></li> </ul> <p><span>.do-Format:</span></p> <div> <ul> <li><a href="../api/records/12200702/draft/files/import_4memory-data-literacy_2024-06-19_11-32.do/content" target="_blank" rel="noopener noreferrer">import_4memory-data-literacy_2024-06-19_11-32.do</a></li> </ul> <p>.r-Format:</p> <div> <ul> <li><a href="../api/records/12200702/draft/files/import_4memory-data-literacy_2024-06-19_11-33.r/content" target="_blank" rel="noopener noreferrer">import_4memory-data-literacy_2024-06-19_11-33.r</a></li> </ul> </div> </div> <p><strong>Aufbereitete Daten:</strong></p> <p>.xlsx-Format:</p> <ul> <li><span><a href="../api/records/12200702/draft/files/2024-06-06-aufbereitete_Daten.xlsx/content" target="_blank" rel="noopener noreferrer">2024-06-06-aufbereitete_Daten.xlsx</a></span> (enthalten sind die Tabellenblätter Ausgangsdatensatz (unbearbeitet), Codebuch (unbearbeitet), überarbeiteter Datensatz und Zusatztabelle Fachbereich</li> </ul> <p>.csv-Format:</p> <ul> <li> <div><a href="../api/records/12166939/draft/files/Dokumentation.csv/content" target="_blank" rel="noopener noreferrer">Dokumentation.csv</a></div> </li> <li><a href="../api/records/12166939/draft/files/Zusatztabelle_Fachbereich.csv/content" target="_blank" rel="noopener noreferrer">Zusatztabelle_Fachbereich.csv</a></li> <li><span><a href="../api/records/12200702/draft/files/%C3%BCberarbeiteter%20Datensatz.csv/content" target="_blank" rel="noopener noreferrer">überarbeiteter Datensatz.csv</a></span></li> </ul>
Sediment trap time series data for Beaverdam Reservoir and Falling Creek Reservoir in southwestern Virginia, USA 2018 through 2023
Sediment traps were deployed to assess the mass and composition (lithium, sodium, magnesium, aluminum, potassium, calcium, iron, manganese, copper, strontium, barium, total organic carbon, and total nitrogen) of settling particulates in the water column of two drinking water reservoirs—Beaverdam Reservoir and Falling Creek Reservoir, both located in Vinton, Virginia, USA. Sediment traps were deployed at two depths in each reservoir to capture both epilimnetic and hypolimnetic (total) sediment flux. The particulates were collected from the traps approximately fortnightly from April to December from 2018 to 2023, then filtered, dried, and analyzed for lithium, sodium, magnesium, aluminum, potassium, calcium, iron, manganese, copper, strontium, and barium (2018 to 2023) and total organic carbon and total nitrogen (2018 to 2022, due to instrument repairs). Beaverdam and Falling Creek are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia. The sediment trap dataset consists of logs detailing the sample filtering process, the mass of dried particulates from each filter, and the raw concentration data for lithium (Li), sodium (Na), magnesium (Mg), aluminum (Al), potassium (K), calcium (Ca), iron (Fe), manganese (Mn), copper (Cu), strontium (Sr), barium (Ba), total organic carbon (TOC) and total nitrogen (TN). The final products are the calculated downward fluxes of solid Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba, TOC, and TN during the aforementioned deployment periods.
Micrometeorological data from Etosha Heights Conservation Centre, Namibia, 2023-ongoing
This data set contains half-hourly micrometeorological data from six weather stations distributed across Etosha Heights Private Reserve in northern Namibia. Data collection began at the end of May 2023, and is ongoing. The data are part of a project funded by Colgate University's Picker Interdisciplinary Science Institute, in collaboration with Giraffe Conservation Foundation and the Namibia University of Science and Technology, aimed at better understanding animal movement. That data are being coupled with gps data from a variety of animals within the reserve and adjacent Etosha National Park.
Greenhouse gas partial pressure (CO2, CH4, N2O) and environmental variables (physical, chemical, and biological) measured in urban ponds of Barcelona during summer and winter (2023-2024)
This dataset provides information on the partial pressure of greenhouse gases (CO₂, CH₄, and N₂O) measured in 41 artificial urban ponds—28 naturalized and 13 non-naturalized—using the headspace technique. Additionally, GPS coordinates, as well as physical, chemical, and biological variables for each pond, are included. Data were collected during the summer and winter seasons, during daytime. Furthermore, a subset of 16 ponds (8 naturalized and 8 non-naturalized) was also sampled at night in both seasons. All samples were taken from the water surface.
Tortuga Restoration Spawning Surveys on the Stanislaus River, Stanislaus County, CA, 2023-2024
The East Stanislaus Conservation District and Cramer Fish Sciences, funded by a Bureau of Reclamation Central Valley Project Improvement Act program grant, are designing, constructing, and monitoring the Tortuga Salmonid Habitat Restoration project, aimed at improving juvenile rearing and adult spawning habitat on the lower Stanislaus River for Central Valley fall-run Chinook Salmon ( Oncorhynchus tshawytscha ) and steelhead ( O. mykiss ). The project is located approximately 68 km upstream from the confluence with the San Joaquin River. The project has the potential to create approximately 3.18 acres of seasonally inundated rearing habitat, 0.44 acres of perennial in-channel rearing habitat, and 0.44 acres of spawning habitat. The project is expected to be constructed in 2025 or 2026, with two years of post-project monitoring following construction. This work supports effectiveness monitoring of the project including spawning and rearing (snorkel) surveys and is ongoing.
ScienceDex guides
Understand access before you commit
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.