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2,532 results for “at present”
Mass and Nutrient Loss in Decomposing Hardwood Boles on Watershed 1 at the Hubbard Brook Experimental Forest, 1990 - present
In 1990-1991 segments of boles from felled sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis) and American beech (Fagus grandifolia) trees were placed in the field to study the rate of decomposition and nutrient loss (or gain) over time. The segments incubated in the field, ranging from 0.5-1.3 meters in length, were paired with fresh segments from the same trees. The fresh segments were taken to the lab shortly after felling, dried, weighed and subsampled. Fresh samples of wood and bark were collected separately. Incubated bole segments were collected in 1993 (T1), 1997 (T2), 2001 (T3), 2007 (T4) and 2015/2016 (T5). The whole bole segments were transported to the lab, measured, dried and weighed to determine mass loss. Subsamples of the bole wood and bark were collected for chemical analysis, including C, N, H, Ca, Mg, K, Si, Al, Pb, Zn, Mn and Fe. Chemical analyses were conducted concurrently on the fresh (T0) and incubated samples. This data set includes the masses of the fresh and incubated boles along with the concentrations of the chemical analytes. Element pools in the boles can be calculated by multiplying the concentrations by the mass values. This data set includes chemical data for samples collected in 1993, 2001, and 2007 and their paired fresh samples. Samples from 1997 were measured for mass, but inadvertently discarded prior to chemical analysis. 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 USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Daily Streamflow by Watershed, 1956 - present
Streamflow at 9 watersheds (12-77 Hectares) within the Hubbard Brook Experimental Forest in New Hampshire, USA has been measured continuously from as early as 1955. Streams are gaged with V-notch weirs, in some cases in combination with rectangular San Dimas flumes, at the outlet of each watershed. Through 2012, stage heights were recorded by means of a mechanical spring-wound clock and pen on a strip-chart recorder. Beginning January 1, 2013, these analog chart recorders were replaced with digital sensors. Overlap with both measurement techniques occurred for several years at all weirs. Streamflow data were gathered by the Hubbard Brook Experimental Forest and contributed to 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 USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: 15 Minute Solar Radiation Measurements, 2014 - present
Beginning in 2014, solar radiation sensors were implemented at the Hubbard Brook Experimental Forest to measure solar radiation at 15-minute intervals. Two collocated LiCor sensors were installed at Station 1 in July 2014. The 15-minute record for Headquarters begins in 2018 with a single sensor. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: 15-minute Precipitation Measurements, 2011 - present
Beginning in 2011, ten electronic weighing rain gauges were progressively implemented at the Hubbard Brook Experimental Forest to measure precipitation at 15-minute intervals. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: 15-Minute Air Temperature Record, 2014 - present
Air temperature is measured at 15-minute intervals in six clearings throughout the Hubbard Brook experimental watersheds and at the Headquarters Station. Beginning in 2014, digital sensors housed in aspirated solar radiation shields collected air temperature measurements. These data are gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: 15 Minute Wind Speed and Direction Measurements, 2012 – present
Wind speed and direction have been recorded at 15-minute resolution by an R.M. Young company wind sensor at three locations within Hubbard Brook Experimental Forest since 2012. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Routine Seasonal Phenology Measurements, 1989 - present
Spring and Fall leaf phenology observations have been made at 9 locations at the Hubbard Brook Experimental Forest since 1989. Timing and progression of spring leaf out and fall senescence are recorded for 3 dominant tree species, sugar maple, yellow birch, and beech, in treated and untreated watersheds and high and low elevations. Weekly measurements are taken during the active period of the fall or spring season. These data were gathered at the Hubbard Brook Experimental Forest in North Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest
Bird abundances have been determined from timed censuses, territory maps and nest locations at the Hubbard Brook Experimental Forest from 1969 to the present. This data set includes counts of the number of adult birds (males and females) per 10 ha at HBEF (1969 - present) and on three additional plots within the White Mountain National Forest (1986 - 2000). 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 USDA Forest Service, Northern Research Station.
SBC LTER: Reference: Sea-surface water temperature, Santa Barbara Harbor, Santa Barbara, CA, USA, 1955 to present, ongoing
The SBC-LTER has access to data on seawater temperature collected at Santa Barbara Harbor, Santa Barbara, CA, USA through the Scripps Institution of Oceanography Manual Shore Stations program. The SIO Manual Shore Stations program provides data and information about this shore station. For further information, please visit the SIO Manual Shore Stations website at https://library.ucsd.edu/dc/object/bb07606686. Please note: manual shore station data is updated periodically, not continuously. Funding for the Shore Stations Program provided by the California Department of Parks and Recreation, Natural Resources Division, Award# C22820005. Contact shorestation@ucsd.edu if you have questions
ChinaHighCO: Daily Seamless 1 km Ground-Level CO Dataset for China (2019–Present)
<p>ChinaHighCO is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level CO dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m<sup>-3</sup>, and a mean absolute error (MAE) of 0.16 mg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighCO<sub> </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641530">https://doi.org/10.5281/zenodo.4641530</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)
<p>ChinaHighSO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level SO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 4.68 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighSO<sub>2</sub> dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighSO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2013–2018 </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641538">https://doi.org/10.5281/zenodo.4641538</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighO₃: Daily Seamless 1 km Ground-Level O₃ Dataset for China (2000–Present)
<p>ChinaHighO<sub>3</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level maximum daily 8-hour average (MDA8) O<sub>3</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.89, a root-mean-square error (RMSE) of 15.77 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 10.48 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighO<sub>3</sub> dataset in your scientific research, please cite the following references (Yang et al., RSE, 2025; Wei et al., RSE, 2022):</p> <ul> <li>Yang, Z., Li, Z., Cheng, F., Lv, Q., Li, K., Zhang, T., Zhou, Y., Zhao, B., Xue, W., and Wei, J. <a href="https://weijing-rs.github.io/publications/Yang_et_al-RSE-2025.pdf" target="_blank" rel="noopener">Two-decade surface ozone (O<sub>3</sub>) pollution in China: enhanced fine-scale estimations and environmental health implications</a>. <em>Remote Sensing of Environment</em>, 2025, 317, 114459. https://doi.org/10.1016/j.rse.2024.114459</li> </ul> <ul> <li> <p>Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2022.pdf">Full-coverage mapping and spatiotemporal variations of ground-level ozone (O<sub>3</sub>) pollution from 2013 to 2020 across China</a>. <em>Remote Sensing of Environment</em>, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775</p> </li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
ChinaHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for China (2000–Present)
<p>ChinaHighPM<sub>2.5</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>2.5</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.92, a root-mean-square error (RMSE) of 10.76 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 6.32 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>2.5</sub> dataset in your scientific research, please cite the following references (Wei et al., RSE, 2021; Wei et al., ACP, 2020):</p> <ul> <li> <p>Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2021.pdf">Reconstructing 1-km-resolution high-quality PM<sub>2.5</sub> data records from 2000 to 2018 in China: spatiotemporal variations and policy implications</a>. <em>Remote Sensing of Environment</em>, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136</p> </li> <li> <p>Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2020.pdf">Improved 1 km resolution PM<sub>2.5</sub> estimates across China using enhanced space-time extremely randomized trees</a>. <em>Atmospheric Chemistry and Physics</em>, 2020, 20(6), 3273–3289. https://doi.org/10.5194/acp-20-3273-2020</p> </li> </ul> <p><strong>The data is continuously updated, and</strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2022)</a></strong></p> <p><strong> </strong> all (including <strong>daily</strong>) data for the year <strong>2023</strong> is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2023)</a></strong></p> <p><strong> </strong>all (including <strong>daily</strong>) data for the year <strong>2024 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10472665">ChinaHighPM2.5 (2024)</a></strong></p> <p> more data is coming soon...</p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ChinaHighPM₁₀: Daily Seamless 1 km Ground-Level PM₁₀ Dataset for China (2000–Present)
<p>ChinaHighPM<sub>10</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM<sub>10</sub> dataset for China <strong>from 2000 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 11.22 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>10</sub> dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021):</p> <ul> <li> <p>Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EI-2021.pdf">The ChinaHighPM<sub>10</sub> dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China</a>. <em>Environment International</em>, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290</p> </li> </ul> <p><strong>The data is continuously updated, and</strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2022 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2022)</a></strong></p> <p> all (including <strong>daily</strong>) data for the year <strong>2023 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2023)</a></strong></p> <p><strong> </strong>all (including <strong>daily</strong>) data for the year <strong>2024 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.10475457">ChinaHighPM10 (2024)</a></strong></p> <p> more data is coming soon...</p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
IPBES Assessment of the diverse values and valuation of nature - Figures presented in the summary for policymakers
<p>These figures are an integral part of the Summary for policymakers of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"
<p>These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below.</p> <p>The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps.</p> <p>The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where:</p> <p><malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting).</p> <p><age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr).</p> <p><prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population).</p> <p>The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies.</p> <p>The files can be read into python using:</p> <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> <p>which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters <span class="math-tex">\(F, \gamma, \overline{\log(\rho)}\)</span>, and <span class="math-tex">\(\sigma_{\log(\rho)}\)</span>, respectively.</p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 4
<p>These tables are an integral part of Chapter 4 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 3
<p>These tables are an integral part of Chapter 3 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
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.