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2,322 results for “2006”
MCR LTER: Coral Reef Resilience: Live and Dead Pocillopora and Acropora Coral Colony Time Series from 2006 to 2011
These data describe the abundance, size structure, and morphologies of living and dead corals belonging to the genera Pocillopora and Acropora on the forereef (depth = 10 meters) in 2006, 2009, 2010, and 2011. Data were derived from a randomly chosen subset of photo quadrats associated with knb-lter-mcr.4. For each quadrat, individual coral colonies were identified to genus, scored as living or dead, and the total area of their footprint calculated. In addition, branch morphology was scored on a scale from 1 to 3, with 1 representing very tight spacing, and 3 representing open spacing among adjacent branches. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
MCR LTER: Coral Reef: Gump Station Meteorological Data, ongoing since 2006
These data provide meteorological information measured at the GUMP research station on the north shore of Moorea island, French Polynesia. Data collection began in August 2006 and include air temperature, relative humidity, wind speed and direction, solar radiation, atmospheric pressure, and integrated rainfall. All sensors are sampled every 5 minutes. Post-processing of these data is limited to some unit conversion and exclusion of corrupted data records. Aggregate data are provided in daily, weekly, monthly, and yearly time bins. ClimDB data for station GUMPM were derived from the same data source.
North Temperate Lakes LTER: High Frequency Data: Meteorological, Dissolved Oxygen, Chlorophyll, Phycocyanin - Lake Mendota Buoy 2006 - current
The instrumented buoy on Lake Mendota is equipped with limnological and meteorological sensors that provide fundamental information on lake thermal structure, weather conditions, and lake metabolism. Data are collected every minute. Hourly and daily averages are derived from the high resolution (1 minute) data. Hourly and daily values may not be current with high resolution data as they are calculated at the end of the season. Meteorological sensors measure wind speed, wind direction, relative humidity, air temperature, and photosynthetically active radiation (PAR). Not all sensors are deployed each season. A list of sensors used since the first deployment in 2006 is provided as a downloadable CSV file. The buoy is removed during the winter when the lake is ice-covered (typically Dec-Apr). Lake temperature data collected at the same buoy site can be found in an ancillary dataset. Number of sites: 1. Location lat/long: 43.0995, -89.4045 Notable events: 2017 - A boating mishap caused the loss of air temperature, relative humidity, and wind sensors between May 28 and July 11. The dissolved oxygen sensor had significant biofouling from algae and zebra mussels. 2019 - A YSI EXO2 sonde was added to the buoy and includes DO, chlorophyll, phycocyanin, specific conductance, pH, fDOM, and turbidity sensors. The chlorophyll and phycocyanin sensors replace Turner Cyclops 7 fluorometers that had been in use in prior years. Both sets of sensors output RFU, but have significant magnitude differences. The YSI pH, DO, and specific conductance sensors were cleaned and recalibrated every two weeks. 2020 - Cleaning and calibration of the YSI sensors occurred nearly every week. The dissolved CO2 sensor was not operating between July 2 and September 17. 2021 - Due to power and communications issues, the buoy was not operating August 22-31, and data is intermittent between November 8 and December 3. An effective method to keep the underwater PAR sensor mostly free of biofouling algae has
North Temperate Lakes LTER: High Frequency Water Temperature Data - Lake Mendota Buoy 2006 - current
The instrumented buoy on Lake Mendota is equipped with a thermistor chain that measures water temperature. In 2006, the thermistors were placed every half-meter from the surface through 7m, and every meter from 7m to 15m. Since 2007, the thermistors were placed every half-meter from the surface through 2m, and every meter from 2m to 20m. The sensor at the water surface is as close to the surface as feasible. A list of sensors used since the first deployment in 2006 is provided as a downloadable CSV file. Hourly and daily water temperature averages are computed from high resolution (1 minute) data.The buoy is removed during the winter when the lake is ice-covered (typically Dec-Apr). Meteorological and limnological data collected at the same buoy site can be found in an ancillary dataset. Sampling Frequency: one minute. Number of sites: 1. Location lat/long: 43.0995, -89.4045 Notes: The thermistor string failed in June 2014, so there is not data between June 17, 2014 and the start of the 2016 season. The thermistor string failed again in August 2023. It was replaced by Hobo temp loggers at 0, 5,10, 15, 20 meters depth for the remainder of 2023.
North Temperate Lakes LTER Soil Temperature - Woodruff Airport 2006 - current
Soil temperature data are being gathered at a site at the Noble F. Lee municipal airport located at Woodruff, WI. Soil temperature is measured at depths of 0.05m, 0.1m and 0.5m at 1-minute intervals. High resolution data are collected (typically at 10 minute intervals) along with 1-hour and 24-hour averages. Daily minimum and maximum soil temperatures and the times these occur are reported for these same depths. Data are automatically updated into the database every six hours. Prior to August 2006, only hourly averaged data are available. Starting in 2008, soil temperatures are only available from 0.5m depth. Sampling frequency: varies for instantaneous samples; averaged to hourly and daily values from one minute samples. Number of sites: 1. Data collection failure caused data loss for the first half of 2024.
Kelp metapopulations: Semi-annual time series of giant kelp patch area, biomass and fecundity in southern California, 1996 - 2006
These data describe the patch-scale canopy biomass and population fecundity of giant kelp, Macrocystis pyrifera, in southern California, USA, from 1996¬ to 2007. Biomass of the surface canopy was estimated using diver-calibrated Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus satellite imagery. Fecundity was estimated from canopy biomass pixel data using a seasonally-adjusted relationship between the diver-measured density of giant kelp spore-bearing tissue and the Landsat estimate of canopy biomass density using data collected across five years at the San Clemente Artificial Reef, located offshore of San Clemente, California, USA. Landsat pixel-scale estimates of giant kelp biomass and fecundity were summed across space for each giant kelp patch and averaged across time separately with two semesters each year (January–June and July–December). The location and area of each giant kelp patch are also provided. These data were described in <ulink url="http://dx.doi.org/10.1890/15-0283.1">Castorani, M. C., D. C. Reed, F. Alberto, T. W. Bell, R. D. Simons, K. C. Cavanaugh, D. A. Siegel and P. T. Raimondi. Connectivity structures local populations dynamics: a long-term empirical test in a large metapopulation system. Ecology. DOI: 10.1890/15-0283.1</ulink> These data are part of the NSF collaborative project: The effect of inbreeding on metapopulation dynamics of the giant kelp, Macrocystis pyrifera (funded wholly or part by NSF Awards OCE-1233283, 1233288, 1233839).
Kelp metapopulations: Semi-annual time series of spore dispersal times among giant kelp patches in southern California, 1996 - 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among patches in southern California, USA, from 1996 to 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations among patches were estimated for 6-month periods (January - June and July - Dececember, 1996 - 2006) using minimum mean transit times connecting source and destination connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS). Minimum transport times between giant kelp patches were assumed to be proportional to minimum transport times between ROMS cells and the alongshore distance between giant kelp patches
SBC LTER: Reef: Community structure and productivity of subtidal turf and foliose algal assemblages at Naples Reef, 2006
This dataset contains abundance, primary production and respiration of macroalgal and turf assemblages at Naples Reef (Santa Barbara County, CA) during 2006. It includes abundance of macroalgae is in terms of biomass (dry weight), and abundance of animals is in numbers of individuals and biomass (ash-free dry weight). Primary production and respiration of the benthos were measured in situ as changes in oxygen in closed chambers that covered 0.1m2 of the bottom. Species richness data are for macroalgae only. Abundance and diversity were measured for the same plots where oxygen measurements were recorded. These data were presented in: <ulink url="http://dx.doi.org/10.3354/meps08131">Miller, R.J., D. Reed, and M. Brzezinski. 2009. Community structure and productivity of subtidal turf and foliose algal assemblages. Marine Ecology Progress Series 388:1-11 doi: 10.3354/meps08131</ulink>.
S13 | EUCOSMETICS | Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)
<p>This is the collection associated with list S13 EUCOSMETICS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p><strong>Combined Inventory of Ingredients Employed in Cosmetic Products (2000) and Revised Inventory (2006)</strong></p> <p>Merged Cosmetics <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017.csv">CSV</a> (4/05/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/eucosmetics">EU Cosmetics List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Merged_CosmeticProducts_04052017_InChIKeys.txt">Merged Cosmetics InChIKeys</a> (4/05/2017)</p> <p>The scientific committee on cosmetic products and non-food products Intended for consumers - <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/SCCNFP038900_INCI-2000.pdf">SCCNFP/0389/00 Final</a> and Commission <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Decision_2006_257_EC.pdf">Decision 2006/257/EC</a> amending the Decision 96/335/EC. Provided by Peter von der Ohe, UBA, curated by Reza Aalizadeh, University of Athens.</p> <p>Update 12 May 2020: Added the source data from both reports (INCI-2000 and Decision 96/335/EC), including the function information and undefined structures. Update 28 May 2020: removed incomplete structural information from Decision 96/335/EC files. Update 24/7/2020: corrected one synonym reported by PubChem (GFMHHNOUDDHEOO-UHFFFAOYSA-N).</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2006): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2006. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2006): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2006. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2006): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2006. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2006): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2006. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Air/Snow temperature vertical profiles at different sites in Livingston Island, Antarctica (2006-2023)
<p>Air or seasonal snow temperature data collected at different heights above the ground (2.5, 5, 10, 20, 40, 80, and 160 cm), generally recorded every 3 hours between 2006 and 2023, using an array of temperature micro-loggers (iButton models by Maxim) mounted along a vertical wooden mast. These measurements were taken at various stations of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor the thermal dynamics of frozen soils on Livingston Island, South Shetland Islands, Antarctica.</p>
Sea level between 21000 and 450 BP, from Sathiamurthy and Voris 2006
<p>Sea level extracted and interpreted from Sathiamurthy, Edlic and Harold K. Voris 2006. “Maps of Holocene Sea Level Transgression and Submerged Lakes on the Sunda Shelf,” Tropical Natural History Supplement, 2, p. 1-44.</p> <p> </p>
ERA5-Land selected indicators daily aggregates for Africa, 2006
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2006.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
2006_2022_MODIS _EnhancedVegetationIndex
<p>MODIS decadal and monthly of Enhanced Vegetation Index, 5km, 2006-2022. </p> <p>Abstract: this is a reduced 5km resolution version of the 1km data used for Fourier Processed outputs provided in other datasets. It is designed for use with administraytive level analysis which need to used covariate data that temporally matches the modelled variable. The data are directly extracted from the NASA archive (MOD13C1 and MOD13C2) and windowed for the E4warning study area.</p>
Trento 1936 - Building 2006
<u>Coordinates</u>: N/A <br><u>Length</u>: 18.41 m<br><u>Width</u>: 13.44 m<br><u>Height</u>: 21.94 m<br><u>Points</u>: 14 <br><u>Vertices</u>: 72 <br><u>Primitives</u>: 24 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12695446/files/building_2006.glb/content">building_2006.glb</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695446/files/building_2006.obj/content">building_2006.obj</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12695446/files/11577947_edm.xml/content">11577947_edm.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12695446/files/11577947_edm.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12695446/files/11577947_metsmods.xml/content">11577947_metsmods.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12695446/files/11577947_metsmods.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12695446/files/building_2006_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12549166">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12695446">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
CLDF dataset derived from Zhao's "Investigations of Zhaozhuang Bai" from 2006
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zhao, Yanzhen (2006): Zhàozhuāng Báiyǔ miáoxiě yánjiū 趙莊白語描寫研究 [Investigations of Zhaozhuang Bai]. Běijīng: Zhōngyāng Mínzú Dàxué.</p> </blockquote>
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.