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2,322 results for “2006”
SBC LTER: CTD profiles from UNOLS cruises in the Santa Barbara Channel: LTER15, 2006-02-02 to 2006-02-09
The data described here were collected on LTER15 which took place from 2006-02-02 to 2006-02-09 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 4 basic types of measurements: Profiles at 25 grid stations unique to the SBCLTER and 7 cross-channel stations (also occupied by the Plumes and Blooms project). Measurements include primary production, basic CTD parameters (Seabird 911-plus), inorganic nutrients, phytoplankton pigments, particulate organic carbon and nitrogen, natural abundances of N and C isotopes, and occasionally biogenic and lithogenic silica, optics, and/or dissolved organic carbon. Two-dimensional sections of oceanic constituents are recorded in the top 120m with an undulating towed CTD scanning system (Scanfish) on both along shore and cross-channel transects. Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous climate measurements. Underway acoustic dopler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
SBC LTER: CTD profiles from UNOLS cruises in the Santa Barbara Channel: LTER16, 2006-04-26 to 2006-05-03
The data described here were collected on LTER16 which took place from 2006-04-26 to 2006-05-03 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 4 basic types of measurements: Profiles at 25 grid stations unique to the SBCLTER and 7 cross-channel stations (also occupied by the Plumes and Blooms project). Measurements include primary production, basic CTD parameters (Seabird 911-plus), inorganic nutrients, phytoplankton pigments, particulate organic carbon and nitrogen, natural abundances of N and C isotopes, and occasionally biogenic and lithogenic silica, optics, and/or dissolved organic carbon. Two-dimensional sections of oceanic constituents are recorded in the top 120m with an undulating towed CTD scanning system (Scanfish) on both along shore and cross-channel transects. Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous climate measurements. Underway acoustic dopler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP. On Cruise LTER16, additional CTD samples were collected to document a red tide, the area off Naples Reef, and at Campus Point. On this cruise, additional stations and are identified as RED001-RED007, NAPLES001-010, and CAMPUSPOINT001, respectively
SBC LTER: Meteorological and sea surface data from the R/V Pt. Sur Underway Data Acquisition System (UDAS) in the Santa Barbara Channel: LTER15, 2006-02-02 to 2006-02-09
The data described here were collected on LTER15 which took place from 2006-02-02 to 2006-02-09 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 2 basic types of measurements: Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous atmospheric climate measurements. Underway acoustic doppler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
SBC LTER: Meteorological and sea surface data from the R/V Pt. Sur Underway Data Acquisition System (UDAS) in the Santa Barbara Channel: LTER16, 2006-04-26 to 2006-05-03
The data described here were collected on LTER16 which took place from 2006-04-26 to 2006-05-03 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. There are 2 basic types of measurements: Underway measurements: fluorometry, salinity and temperature are collected at a depth of 3m with a flow-through system which also includes continuous atmospheric climate measurements. Underway acoustic doppler current profiles were collected using a RDInstruments Workhorse 300kHz and RDInstruments Ocean Surveyor 75kHz ADCP.
SBC LTER: CTD cross-sections (top 120m) from UNOLS Cruises in the Santa Barbara Channel: LTER15, 2006-02-02 to 2006-02-09
The data described here were collected on LTER15 which took place from 2006-02-02 to 2006-02-09 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. Towed CTD package Two-dimensional sections of oceanic constituents are recorded in the top 120m with an undulating towed CTD scanning system (Scanfish) on both along shore and cross-channel transects.
SBC LTER: CTD cross-sections (top 120m) from UNOLS Cruises in the Santa Barbara Channel: LTER16, 2006-04-26 to 2006-05-03
The data described here were collected on LTER16 which took place from 2006-04-26 to 2006-05-03 on the RV Pt. Sur. Cruises have been conducted in the Santa Barbara Channel, California, USA, 3-4 times per year since March, 2001 and are approximately 7 days in length. Towed CTD package Two-dimensional sections of oceanic constituents are recorded in the top 120m with an undulating towed CTD scanning system (Scanfish) on both along shore and cross-channel transects.
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
2006_2009_MODIS_Phenology
<p>This dataset supercedes the previousl MODIS Phenology datasets based on MODIS V4 data lebelled 'Green up' and 'senescence'.</p> <p>A number of coverages have been processed and extracted from the global MODIS Land Cover Dynamics datasets derived by Boston University (http://www.bu.edu/lcsc/research/land-cover-dynamics) from the NASA MOD12Q2 MODIS tiled phenology layers (https://lpdaac.usgs.gov/products/modis_products_table/mcd12q2). A detailed user guide (http://www.bu.edu/lcsc/files/2012/08/MCD12Q2_UserGuide.pdf, and http://modis.gsfc.nasa.gov/data/atbd/atbd_mod12.pdf) has been released by the Boston University team which provides detailed technical specifications and describes the methods used to calculate the layer values.</p> <p>In summary, these datasets for version 5 were produced twice a year (January and June) between 2001 and early 2010. For each date, a series of parameters were calculated of which five are provided here for 2006 to 2010: Green Up and Senescence , which represent the dates when new green vegetation started be detected at the beginning of a growth cycle, and when the fall in greenness stopped at the end of the cycle.</p> <p>Because some areas have more than once cycle a year, two images are produced for each date: Cycle 1 and Cycle2 . Thus, the Cycle 1 green up image for January 2006 contains the date (expressed as days from January 1 2000) of the green up event for the first cycle occurring between the end of June 2005 and the beginning of July 2006, whilst the Cycle 2 image contains the date for the green up event of the second cycle occurring in that period.</p> <p>This can be confusing, for a detailed explanation with examples please refer to the document included within this download "MODIS PHENOLOGY DATA DESCRIPTION_v2.doc".</p>
IPCC Climate Zones (from the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories)
<p><strong>Description</strong></p> <p>These data (re)create spatial data for the 2019 IPCC Climate Zones, shown in <em>Figure 3A.5.1</em> of <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/pdf/4_Volume4/19R_V4_Ch03_Land%20Representation.pdf">Chapter 3: Consistent Representation of Lands</a> in <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/vol4.html">Volume 4: Agriculture, Forestry and Other Land Use</a> of the <a href="https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html">2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories</a>. I recreated these data because I could not readily identify the data in a spatial format online, a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p>Resolution: 0.5 arc degree</p> <p>CRS: lon/lat WGS 84</p> <p><strong>If you use these data please ensure you also cite the IPCC</strong> - Calvo Buendia, E et al. (2019). 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC, Switzerland.</p> <p> </p> <p><strong>Methods</strong></p> <p>The data were derived using the classification scheme shown in <em>Figure 3A.5.2</em> based on the gridded Climate Research Unit (CRU) Time Series (TS) monthly climate data (<a href="https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.3711">Harris et al., 2014</a>) for the period from 1985 to 2015 following the methods described in <em>Annex 3A.5 Default climate and soil classifications </em>of the above Chapter. All data were processed in <em>R</em> version 4.2.1, with the packages <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> (v0.4.2), <a href="https://cran.r-project.org/web/packages/lubridate/index.html"><em>lubridate</em></a> (v1.8.0), <a href="https://cran.r-project.org/web/packages/magrittr/index.html"><em>magrittr</em></a> (v2.0.3), and <a href="https://cran.r-project.org/web/packages/terra/index.html"><em>terra</em></a> (v1.6-7)<em> </em>attached. The full session info is included as a <em>.txt</em> file. As these methods are not exhaustively described in the Annex, the following assumptions were made:</p> <ul> <li><a href="http://http://dx.doi.org/10.5285/c311c7948e8a47b299f8f9c7ae6cb9af">CRU TS3.25</a> was used as the most recently published data (published on 2017-09-22) that could have been incorporated into the Refinement. Other possibilities include CRU TS3.24 (which are the first data to include 2015), or CRU TS4.00 or CRU TS4.01 (both of which were published in parallel to 3.24 and 3.25). These data were all investigated, and CRU TS3.25 produced results that were the most visually similar to the published <em>Figure 3A.5.1</em> (though non-identical).</li> <li>As the methods did not mention a preferred elevation data source, the <a href="https://cran.r-project.org/web/packages/elevatr/index.html"><em>elevatr</em></a> R package was used to obtain data at zoom level 2 (approx resolution of 0.15 arc degree), that was then resampled to match the 0.5-degree resolution of the CRU data. These data originally come from the <a href="https://www.ngdc.noaa.gov/mgg/global/global.html">ETOPO1 global relief model</a>.</li> </ul> <p> </p> <p><strong>Known discrepancies</strong></p> <ul> <li>The distribution of Tropical Wet and Tropical Moist in South America does not exactly match the original data.</li> <li>There are small discrepancies in Tropical Montane classifications (likely arising from the use of a different elevation layer). These are most noticeable in, but not restricted to, Africa.</li> <li>The classification of Boreal Dry, Polar Dry, and Polar Moist in northern Russia and (to a lesser extent) in northern Canada does not exactly match the original data.</li> <li>There are a small number of Cool Temperate Dry pixels in the UK, and Warm Temperate Dry pixels around Brittany which do not occur in the original data.</li> </ul> <p> </p> <p><strong>Disclaimer</strong></p> <p><strong>I am not affiliated with the IPCC in any way</strong>, I just needed spatial data of the Climate Zones, and could not readily identify any online. This is a problem which has previously been noted by ESDAC, who produced a <a href="https://esdac.jrc.ec.europa.eu/content/support-renewable-energy-directive#tabs-0-description=1">spatial version of <em>Figure 3A.5.1</em> from the original 2006 guidelines</a>.</p> <p> </p> <p><strong>File description</strong></p> <ul> <li><em>README.html</em> - ~this description file.</li> <li><em>IPCC_Climate_Zones_ts_3.25.tif</em> - the output Climate Zones map at 0.5-arc degree resolution based on the CRU TS3.25 data.</li> <li><em>IPCC_Climate_Zones_colour_map.clr </em>- a colour map file to render the output map with the same colours as in the IPCC 2019 Refinement figure.</li> <li><em>IPCC_Climate_Zones_ts_3.25.png</em> - an image file of the output Climate Zones map.</li> <li><em>ipcc_climate_zones_2019.R</em> - the script used to produce these data.</li> <li><em>session_info.txt</em> - the R session info.</li> </ul>
Dataset of reported synthetic conditions for ZIF-8 since 2006 and final characteristics and properties of the particles obtained
<p>This is a dataset generated by mining available literature between 2006 and August 2021. Initial literature search was done using Scopus database, employing a combination of keywords such: "ZIF-8", "Zeolitic+ZIF-8", "Framework+ZIF-8", "Zeolitic+MOF".</p> <p>First, removal of duplicates and review articles was done and the remaining documents were chosen by title+abstract analysis. The dataset contains a total of 254 entries, i.e., 254 individual reported synthesis.</p> <p>Throughout this dataset, the following parameters can be found:</p> <p>-About synthesis conditions: Zinc source and the amount employed for the synthesis in mmol (milli-moles); 2-methylimidazole in mmol (HmIm); solvent and quantity employed (in mmol). Modulator and quantity employed (in mmol). Please note that quantities in milli-moles were calculated by hand in most of the cases, since reported data was expressed in different units. Reaction temperature (in °C), reaction time (min) and stirring condition (YES-NO-Initial-time). Finally, reports were classified as "systematic" (or not) based on wether the scope of the work was to explore different synthetic conditions.</p> <p>-About ZIF-8 characteristics: information is mainly focusing on structure-related characteristic, namely the particle morphology (classified as Faceted, poor-faceted, quasispherical, aggregated) and the particle´s size. Reported sizes where classified by the different techniques employed. Finally, Surface area determined by BET formalism was also included.</p>
Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2006-2008)
<p>This data is part of the <em>Monthly aggregated Water Vapor MODIS MCD19A2 (1 km)</em> dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.</p><p><strong>General Description</strong></p><p>The monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:</p><ul><li><strong>Monthly time-series:</strong><br>Derived from <em>MCD19A2 v061</em>, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li><strong>Yearly time-series:</strong><br>Derived from <em>monthly time-series</em>, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li><strong>Long-term data (2000-2022):</strong><br>Derived from <em>monthly time-series</em>, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul><p><strong>Data Details</strong></p><ul><li><strong>Time period:</strong> 2006–2008</li><li><strong>Type of data:</strong> Water vapor column above the ground (0.001cm)</li><li><strong>How the data was collected or derived:</strong> Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li><strong>Statistical methods used:</strong> Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li><li><strong>Coordinate reference system:</strong> EPSG:4326</li><li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.00081, 179.99994, 87.37000)</li><li><strong>Spatial resolution:</strong> 1/120 d.d. = 0.008333333 (1km)</li><li><strong>Image size:</strong> 43,200 x 17,924</li><li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li></ul><p><strong>Support</strong></p><p>If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:</p><ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul><p><strong>Name convention</strong></p><p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p><ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20060101 = 2006-01-01</li><li>Time reference end time: 20081231 = 2008-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>
Permanent plot data for old-growth white pine - hemlock - hardwood forests in the Huron Mountains, Marquette County, Michigan: 2006-2022
This study was initiated by Dr. Dennis A. Riege with a focus on the role of white pine (Pinus strobus) in old-growth mixed white pine-hemlock-northern hardwood forests. Study areas within the lands of the Huron Mt. Club were selected for prominent presence of canopy white pine. Several permanent study plots, totaling about 3.3 ha, were established, with all trees >5 cm diameter at breast height (dbh) identified, mapped and measured. Initiatl establishment was from 2006-2008. All plots were remeasured in 2011, 2016 and 2021-22. Mortality and new recruits were documented in remeasurements. Reference coordinates for each plot are included in 'Methods'. Data tables in this package include all of these measurements. Additional information, including maps of downed logs, is included in material included under 'other entities'.
Fine wood decay studies at the H.J. Andrews and other Forests across the world, 1989 to 2006
This study was established to examine the loss of mass and changes in nutrient content of fine woody debris in various ecosystems. Also examined was the effect of piece size, position (aboveground, belowground, or suspended), and species on loss of mass over time.
Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska - 2006
The Biocomplexity Station was established in 2005 to measure landscape-level carbon, water and energy balances at Imnavait Creek, Alaska. The station is now contributing valuable data to the Arctic Observing Network that was established at two nearby stations. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.
Survey of aspen herbivory and aspen leaf miner (Phyllocnistis populiella) survival and abundance from 2006 to 2022
Leaf-level measurements of herbivory damage on quaking aspen caused by the aspen leaf miner (ALM) and externally-feeding herbivores, and ALM abundance and survival, from four sites near Fairbanks, Alaska over time.
Phoenix Area Social Survey (PASS): 2006
The first Phoenix Area Social Survey (PASS) was conducted as a pilot study in 2001. Our main objective was to examine the reciprocal relationships, or the interplay, between the social and natural environments in an urban ecosystem. In order to begin to understand this complex process, social scientists affiliated with the Central Arizona-Phoenix LTER conducted a pilot survey of 302 residents in 8 neighborhoods in the city of Phoenix. Our central research questions asked how neighborhood social ties, values, and behaviors are connected with one another in ways that reflect willingness to act socially and politically with respect to the environment, and how changing environmental conditions, in turn, affect the quality of human life. The survey measured the social ties of individuals to their communities, values and sentiments regarding communities, behaviors that affect the natural environment, and satisfaction with the quality of life in the area. The second wave of PASS was conducted in 2006 with an expanded sample size in 40 neighborhoods across the metropolitan region. Many of the questions about community were repeated from the pilot study and the new survey added items for perceptions, values, and behavior concerning water supply and conservation; land use, preservation and growth management; air quality and transportation; and climate change and the urban heat island. This data package previously contained data relating to a 2001 iteration of the Phoenix Area Social Survey. Those data are now available at the following location: "Harlan, Sharon; Kirby, Andrew; Nelson, Amy; Hope, Diane; Pijawka, K. David; Bolin, Robert; Rex, Tom R; Larsen, Larissa; Wolf, Shaphard; Hackett, Edward (2016-07-05): Phoenix Area Social Survey (PASS): 2001. Long Term Ecological Research Network. http://dx.doi.org/10.6073/pasta/6849e6e3ecc196de0f3c8491eb375cc3." Datasets in the Phoenix Area Social Survey (PASS) series of long-term studies are discoverable in the LTER data system with
Chlorophyll and phaeopigments measured from discrete bottle samples from CCE LTER process cruises in the California Current System, determined by extraction and bench fluorometry, 2006 - 2024 (ongoing).
Discrete bottle samples taken from various depths in the CCE region are filtered (known volumes) onto GF/F filters onboard the CCE Process cruises (since 2006, ongoing). The filters are placed into culture tubes containing 90% acetone, and the fluorescence of the samples is read on a fluorometer after 24 to 48 hours. The samples are then acidified to degrade the chlorophyll to phaeopigments (non-photosynthetic pigments) and a second reading is taken. The readings prior to and after acidification are used to calculate concentrations of both chlorophyll a and phaeopigments (i.e. phaeophytin).
Dissolved inorganic nutrients from CCE LTER process cruises, including 5 macro nutrients from water column bottle sample, 2006 - 2024 (ongoing).
Dissolved inorganic nutrients (phytoplankton macro nutrients) are measured from water column bottle samples from the CCE region (since 2006, ongoing) and include nitrate, nitrite, silicate, phosphate and ammonium. They are analyzed in seawater using a colorimetric assay in which light absorbance is measured versus known standards, and final concentrations are calculated (in µmol/L).
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Biogeochemical data collected from Northeast Shark Slough, Everglades National Park (FCE LTER) from September 2006 to September 2008
Each site's physical characteristics (pH, water depth and temperature) were measured in the field and recorded. Two water samples (filtered and unfiltered) were collected from each throw, along with a floc sample (if present). All samples were taken back to the lab for analysis (e.g. water content, % organic matter, bulk density, and TP/TN/TC).
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.