Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “2006-2008”
Construction Costs of Carnivorous and Non-Carnivorous Plants at Harvard Forest 2006-2008
Leaf traits, including photosynthetic rates, leaf mass area, and leaf nutrient content covary in a coordinated way for a wide range of plant taxa. This covariation results from trade-offs between the costs of constructing plant tissues and the benefits accrued from photosynthesis. Carnivorous plants have been found to be outliers in the "universal spectrum of leaf traits" because they have very low photosynthetic rates for the amount of nitrogen in their leaves and traps. But no studies have measured simultaneously the actual construction costs of carnivorous traps and rates of photosynthesis to determine the amortization required to recover the investment (the "payback time") and thereby calculate the "marginal gain" of "investing" in carnivorous structures. The objective of this study was to measure construction costs (CCmass, grams of glucose required to build 1g of ash-free dry mass of tissue) and photosynthesis (Amass, nmol CO2 g-1 s-1) for traps, leaves, roots, and rhizomes of 15 carnivorous plant species with differing mechanisms of prey capture and consumption (pitfall traps, snap-traps, sticky pads) grown under greenhouse conditions. Payback time (h) was calculated as the quotient of CCmass and Amass after conversion to nmol of carbon per gram of ash-free dry mass. There were highly significant differences amongst species for CCmass of traps but there were no significant differences for CCmass amongst traps, roots and rhizomes. Mean (+- SD) CCmass for traps (1.14 +- 0.24 g glucose g-1) was significantly lower than the mean CCmass of leaves of 267 non-carnivorous plant species (1.47 +- 0.17 g glucose g-1). However, all 15 carnivorous plants examined in this study had low Amass and thus, the marginal gain of carnivory is small with a long payback time (524-1641 h). Our results of low CCmass for carnivorous traps is contrary to the oft-stated expectation of a high cost to construct elaborate carnivorous traps. Payback time integrates traits used to assess leaf
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>
Plant cover under precipitation and nitrogen manipulation treatments at the Jornada Basin LTER site, 2006-2008
This data package contains plot cover data from a precipitation and nitrogen manipulation experiment conducted on the Jornada Experimental Range from 2006-2008. The objective of the study was to understand the interaction of precipitation and nitrogen fertilization on aboveground net primary productivity (ANPP) legacies. Rain-out shelters or supplemental irrigation were used to create 5 levels of precipitation: -80% and -50% reduced, ambient control, and +50% and +80% increased PPT. Half of the plots were randomly assigned to receive ammonium nitrate fertilizer. Plant cover was sampled with a line intercept method in fall 2006 before any treatments began, then again in fall 2007 and 2008 after treatments had been applied. Also available are soil moisture data from this study in data package knb-lter-jrn.210278001. For further information and results, see: Throop, H., L. G. Reichmann, O. Sala, and S. Archer. (2012), Response of dominant grass and shrub species to water manipulation: an ecophysical basis for shrub invasion in a Chihuahuan desert grassland. Oecologia 169: 373-383. https://doi.org/10.1007/s00442-011-2217-4 Reichmann, L. G., O. E. Sala, and D. P. C. Peters. (2013), Water controls on nitrogen transformations and stocks in an arid ecosystem. Ecosphere 4(1):11. <link xlink:href="https://doi.org/10.1890/ES12-00263.1">https://doi.org/10.1890/ES12-00263.1</link> Reichmann, L.G., Sala, O.E. and Peters, D.P.C. (2013), Precipitation legacies in desert grassland primary production occur through previous‐year tiller density. Ecology, 94: 435-443. https://doi.org/ <link xlink:href="https://doi.org/10.1890/12-1237.1">10.1890/12-1237.1</link>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Illinois EPA Ambient Lake Monitoring Program (ALMP): lake water chemistry for all labs, 2006-2008
The Illinois EPA’s Ambient Lake Monitoring Program (ALMP) was created in 1977 in cooperation with the IL Department of Conservation to assess trends in a select few IL public lakes. The Illinois EPA biologists work out of three Illinois EPA regional offices: Springfield, Marion, and Des Plaines and monitor lakes in their respective areas. Approximately fifty lakes are monitored annually and of those, twenty-five are termed “Core Lakes”. A total of 75 publicly-owned lakes were chosen throughout the state of Illinois as Core Lakes. Previously these lakes were monitored every three years in order to establish a long-term monitoring trends database. In 2008 these lakes and select others were monitored on a five year rotational basis. All lakes are monitored five times yearly; once during the spring runoff and turnover period (April or May), three times during the summer (June, July, August), and once during the fall turnover period (October). Depending on the size of the lake, usually three lake sites are monitored. Monitoring includes collection of water quality and sediment samples as well as, field observation data/measurements such as Secchi disk transparency readings, dissolved oxygen, temperature, quantity of algae, macrophytes, and other key aspects of the lake and sampling event. Water quality samples are collected from one foot below the surface at all sites and two feet above the bottom at only the deepest site. These samples are routinely analyzed for suspended solids, nutrients, and chlorophyll. Additional parameters may be added for a designated period of time at select lakes based on changing data needs.
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.