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8,620 results for “experimentation”
Hubbard Brook Experimental Forest: Chemistry of Streamwater – Monthly Fluxes, Watershed 5, 1963 - ongoing
These data are monthly fluxes of solutes in stream water measured in watersheds of the Hubbard Brook Experimental Forest and are a part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), which is a long-term record of stream and precipitation chemistry and volume. The solute fluxes in stream water are calculated as the product of the volume of stream water and solute concentrations. There are nine gaged watersheds at the Hubbard Brook Experimental Forest, some of which have been subjected to experimental manipulations. The calculation of fluxes is currently supervised by John Campbell (US Forest Service). The long-term stream water record is collected and maintained by the US Forest Service. The collection and management of the long-term stream and precipitation chemistry record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case Western), and Scott Bailey (USFS). Current Financial Support for HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service Northern Research Station. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Chemistry of Streamwater – Monthly Fluxes, Watershed 6, 1963 - ongoing
These data are monthly fluxes of solutes in stream water measured in watersheds of the Hubbard Brook Experimental Forest and are a part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), which is a long-term record of stream and precipitation chemistry and volume. The solute fluxes in stream water are calculated as the product of the volume of stream water and solute concentrations. There are nine gaged watersheds at the Hubbard Brook Experimental Forest, some of which have been subjected to experimental manipulations. The calculation of fluxes is currently supervised by John Campbell (US Forest Service). The long-term stream water record is collected and maintained by the US Forest Service. The collection and management of the long-term stream and precipitation chemistry record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case Western), and Scott Bailey (USFS). Current Financial Support for HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service Northern Research Station. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Chemistry of Streamwater – Monthly Fluxes, Watershed 7, 1965 - ongoing
These data are monthly fluxes of solutes in stream water measured in watersheds of the Hubbard Brook Experimental Forest and are a part of the Hubbard Brook Watershed Ecosystem Record (HBWatER), which is a long-term record of stream and precipitation chemistry and volume. The solute fluxes in stream water are calculated as the product of the volume of stream water and solute concentrations. There are nine gaged watersheds at the Hubbard Brook Experimental Forest, some of which have been subjected to experimental manipulations. The calculation of fluxes is currently supervised by John Campbell (US Forest Service). The long-term stream water record is collected and maintained by the US Forest Service. The collection and management of the long-term stream and precipitation chemistry record was initiated in 1963 by Gene E. Likens, F. Herbert Bormann, Robert S. Pierce, and Noye M. Johnson. HBWatER is currently sustained by Tammy Wooster (Cary IES) and Jeff Merriam (USFS) and the dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), Emily Bernhardt (Duke), Lindsey Rustad (USFS), John Campbell (USFS), Bill McDowell (UNH), Charley Driscoll (Syracuse U.), Mark Green (Case Western), and Scott Bailey (USFS). Current Financial Support for HBWatER is provided by NSF LTREB # 1907683 and the USDA Forest Service Northern Research Station. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Soil water content measurements and rainfall data for plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2011-ongoing
This dataset contains soil volumetric water content data collected starting in 2011 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Soil sensors are installed at surface and deep soil layers in each plot and collect hourly averages of volumetric water content using a time-domain reflectometry method. This dataset contains daily averages. This is an ongoing study and the dataset will be updated yearly.
LTREB experimental chironomid mesocosms at Myvatn, Iceland
During the summer of 2014, we conducted experiments testing whether increasing numbers of chironomid larvae would increase primary production and standing chlorophyll a concentrations. We incubated experimental mesocosms with varying numbers of chironomid larvae for 12 days in July. We tested sediments for chlorophyll a concentrations, as sediments are primarily composed of benthic diatoms. We tested the oxygen production in these mesocosms. We did this by sealing the mesocosms and incubating them in Lake Myvatn for 3 hours, and taking measurements of dissolved oxygen before and after the incubations. We were also interested in whether this increase in food resources might translate to increased growth rates of chironomid larvae at high larval densities. After stocking experimental mesocosms with varying numbers of chironomid larvae, we set these mesocosms in Lake Myvatn for 12 days. We collected the larvae at the end of the 12 day experiment and obtained the average dry weights of the Chironomus islandicus larvae in each mesocosm. We hypothesized that the tubes that chironomid larvae build would be a superior substrate for algal growth, as compared to loose sediments. Because there are two taxa (Chironomus islandicus and Tanytarsus gracilentus) that are overwhelmingly dominant at our study site, we wondered whether there would be differences in this effect between the two species. We stocked mesocosms with larvae from one of the two species, and mesocosms were then incubated in Lake Myvatn. We collected sediments and larval tubes from each mesocosm and tested their chlorophyll a concentrations. We hypothesized that one mechanism that chironomid larvae might alleviate algal nutrient limitation by depositing concentrated nutrients near algae in the form of larval excretions. We collected chironomid larvae from Lake Myvatn and placed them in distilled water. We then sieved out the larvae and their fecal passings, and transported the water samples to Madison, WI, USA,
Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016
This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Experimental data for "Deep Learning Methods for Colloidal Silver Nanoparticle Concentration and Size Distribution Determination from UV-Vis Extinction Spectra"
<p>Testing data (experimental data) for neural networks published in preprint https://doi.org/10.48550/arXiv.2404.10891</p> <p>The UV-VIS-NIR spectral data was also used in the dissertation of Nadzeya Khinevch, titled "Two-dimensional structures of nanoparticles for elements of surface-enhanced Raman scattering substrates".</p> <p>Emails of the corresponding authors:</p> <p>Tomas Klinavičius tomas.klinavicius@ktu.lt</p> <p>Tomas Tamulevičius tomas.tamulevicius@ktu.lt</p>
Steady-state operation dataset of an experimental Wet Cooling Tower pilot plant located at Plataforma Solar de Almería
<p>Repository that contains experimental data obtained from a Wet Cooling Tower (WCT) plant located at <a href="https://www.psa.es/es/index.php">Plataforma Solar de Almería</a>.</p> <p>For the article "Wet cooling tower performance prediction in CSP plants: A comparison between artificial neural networks and Poppe’s model", three experimental campaigns were used, quoting from the article:</p> <blockquote> <p>A total of 132 steady-state experimental points have been obtained thanks to the thorough experimentation conducted. These data cover a large variety of ambient conditions (different seasons, days and nights) and thermal loads (from 27 kW to 207 kW). </p> </blockquote> <p> </p> <p>See <code>README.md</code> for a more detailed description and instructions on how to use the data.</p> <h2><br>License</h2> <p><a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0. Attribution 4.0 International</a></p> <p>If the data is used as part of a scientific publication, please cite the source publication:</p> <div> <div>Serrano, Juan Miguel, Pedro Navarro, Javier Ruiz, Patricia Palenzuela, Manuel Lucas, and Lidia Roca. “Wet Cooling Tower Performance Prediction in CSP Plants: A Comparison between Artificial Neural Networks and Poppe’s Model.” <em>Energy</em> 303 (September 15, 2024): 131844. <a href="https://doi.org/10.1016/j.energy.2024.131844">https://doi.org/10.1016/j.energy.2024.131844</a>.</div> </div>
Dataset: Seasonal field trials of single-seed removal by desert birds from experimental devices in Ñacuñan Reserve (Mendoza, Argentina)
<p>Dataset for the paper: Milesi FA, Lopez de Casenave J & Cueto VR (2018) Which food patches are worth exploring? Foraging desert birds do not follow environmental indicators of seed abundance at small scales: a field experiment. bioRxiv 295923. doi: https://doi.org/10.1101/295923</p> <p>Metadata included within the tab-delimited text file</p>
Vibration-based Monitoring of a Small-scale Wind Turbine Blade Under Varying Climate Conditions. Part I: An Experimental Benchmark
<p>This repository contains all publicly available data related to the experimental part of <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/stc.2660">Sonkyo-Benchmark</a>. The data of each experimental case (R, A, B, C, D, E, F, G, H, I, J, K, L) and temperature point (-15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40) are stored in a zip file named "Case_<em>X</em>_(<em>T</em>)", where <em>X</em> denotes the case label and <em>T</em> refers to the temperature value. Each file "Case_<em>X</em>_(<em>T</em>).zip" contains two folders "Case_<em>X</em>_(<em>T</em>)_1" and "Case_<em>X</em>_(<em>T</em>)_2", wherein the test results from the two sensor layouts are stored. </p>
QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (<span class="math-tex">\(k_y \rho_s\)</span>) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).</p>
Trabecular bone – screw interaction. Micro-CT models and experimental push-in results.
<p>The dataset disclosed herein was employed to build the screw-bone interaction models, specifically for tasks related to screw push-in simulation.</p>
Marcell Experimental Forest 30-minute water table elevation and temperature from transects of wells in the S2 and S6 peatlands, 2018-ongoing
This data publication contains 30-minute water table elevation and temperature data collected along bog to lagg transects within two watersheds at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. The bog to lagg transects are located on the north and south sides of S2 and S6 peatlands and contain three surface water wells each. The water table elevations provide information to calculate the hydraulic gradients that drive flow to and from the bogs. The collection of these data was funded by the US Department of Energy. The research program at Marcell Experimental Forest is managed by the USDA Forest Service Northern Research Station.
Marcell Experimental Forest chemistry of surface water draining the S2 catchment, 1986 - ongoing
This data set is a record since 1986 of chemistry for surface water draining the S2 catchment at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. Unfiltered water is usually collected every one or two weeks as part of the long-term monitoring program of the S2 catchment. Some samples were collected more often for various other studies and are included in this data set. Samples are routinely measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, manganese, strontium), silicon, nutrients (ammonium, nitrate+nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon. Occasionally, stable water and mercury isotopes as well as concentrations of dissolved organic carbon (DOC), bacterial respiration of dissolved organic matter, biodegradable DOC (BDOC), ferrous and ferric iron, total mercury (filtered or unfiltered), methylmercury (filtered or unfiltered), and lead were measured. Ultraviolet (UV) absorbance, a measure of water color or dissolved organic matter optical properties, was also measured for some samples. More solutes and values will be added as additional metadata are documented (pre-1986 to 1992), water samples are collected and analyzed (concentrations and isotopes), or archived water samples are analyzed for stable water isotopes. The MEF is operated and maintained by the USDA Forest Service, Northern Research Station.
Near-surface, soil, and air temperature data acquired across multiple locations on the San Joaquin Experimental Range, California, 2011-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Temperature sensors were located at 23 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running E-W. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor, using HOBO (Onset, www.onsetcomp.com) devices.
Weather station data acquired across multiple locations on the San Joaquin Experimental Range, California, 2011-2017
These weather station records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These weather station records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Weather stations were located at six sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. Three full weather stations (north slope, south slope, and valley floor) monitored precipitation, wind, insolation, temperature, relative humidity, and soil moisture. Three micro stations (west slope, east slope, and ridge) measured soil moisture at -20 cm. Data was recorded on a 10-minute interval using HOBO (Onset, www.onsetcomp.com) devices.
Near-surface, soil, and air temperature data acquired across multiple locations in the Teakettle Experimental Forest, California, 2011-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the Teakettle Experimental Forest (Lat 36.967, Long -119.017, elevation 2000-2800 m, www.fs.fed.us/psw/ef/teakettle/). Temperature sensors were located at 44 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges and valleys. To characterize surface temperature variation within select sites, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens (see garden schematic for details). An additional 33 sites were located across the site by way of a stratified sampling scheme which targeted low, medium, and high elevation areas, low, medium, and high radiation areas, and cold air pooling areas. In June 2012, in order to concentrate sensors in a smaller study area (ease of access and to make this more similar to other sites, 22 sites were "retired," and 7 new sites were installed, for a total of 18 during the remainder of the study. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor. using HOBO (Onset, www.onsetcomp.com) devices.
IISD Experimental Lakes Area: Ice Phenology and Thickness, 1969-2025 (partial start/end years).
The IISD Experimental Lakes Area (IISD-ELA) Ice Phenology and Thickness data package provides two types of ice data from multiple lakes in northwestern Ontario, Canada. Ice phenology is the timing of when lakes freeze over in the fall and thaw out in the spring, as an ice-on and ice-off date each year, and the days of ice duration for each winter. Ice thickness data includes total ice thickness as well as the complex breakdown of individual layers (snow, slush, white ice, black ice). This data package includes both tabular data and metadata files for each type of ice data. The ice phenology table consists of a row for each ice-on date, ice-off date, and ice duration period for each lake, including the associated sampling method and any comments. The ice thickness table has a row for each measurement of a frozen lake on a specific date, including the total ice thickness and the thickness of individual layers as column values for each row. Metadata in this data package include a table of location coordinates and record counts, and an information sheet for ice phenology and one for ice thickness. The table of coordinates and counts is useful to know where the lakes and specific sampling sites are located and as an overview of data availability per lake (Lake 239 has the longest and most consistent data record, for both ice phenology and thickness). The two info sheets provide additional metadata details about the datasets, including background and uses of the datasets, a data dictionary, diagrams, lookup tables, descriptions of methods, and additional references. For data about lake depth, size, and volume, please consult the most recent version of our bathymetry data package: https://portal.edirepository.org/nis/revisionbrowse?scope=edi&identifier=1276 This data package is ongoing—updates will be provided as data are collected from these lakes in subsequent years. If data are not present for a lake you are interested in from IISD-ELA, please get in touch with us. The
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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.