Find research datasets worth reusing

opencc-by-4.0Jan 2019View details →

CLDF dataset derived from Lieberherr and Bodt's "Comparative Wordlists of Kho-Bwa" from 2017

Cite the source of the dataset as: <blockquote> Lieberherr, Ismail and Bodt, Timotheus Adrianus (2017): Sub-grouping Kho-Bwa based on shared core vocabulary. Himalayan Linguistics 16(2). 26-63. URL: https://escholarship.org/uc/item/4t27h5fg </blockquote>

opencc-by-4.0Apr 2023View details →

Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"

Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"

opencc-by-4.0Aug 2024View details →

A new method for approximating fractional derivatives/ integrals as a series of higher-integer-order derivatives - examples and results of applying the method to initial/boundary value problems

The posted research data includes examples of the application of the author's fractional derivative/integral approximation method using the sum of higher integer derivatives. The attached text files contain the numerical solutions of the presented examples, recorded as a set of numerical values obtained from the performed computations. <ul> <li>Example 4.1   \(\begin{cases}     \displaystyle         ^{C}D^{\alpha}_{a+}\sin (x), \\         x \in \langle a, 3\pi \rangle \quad \hbox{and} \quad         \alpha = \{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},     \end{cases}\) </li> <li>Example 4.2  \( \begin{cases}     \displaystyle         I^{\alpha}_{0+} e^{-x}\cos 7x, \\         x \in \langle 0,1\rangle \quad \hbox{and} \quad         \alpha =\{1.0,\ 1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0 \},     \end{cases} \) </li> <li>Example 5.1  \(\begin{cases}     ^{C} D_{0+}y(x)+2y(x)=x+ \frac{2x^{\alpha+1}}{\Gamma(\alpha+2)},\\     x\in\langle0,1\rangle, \\     y(0) = 0; \quad y(1) = \frac{1}{\Gamma(\alpha+2)}, \\     \alpha = \{1.2,\ 1.4,\ 1.6,\ 1.8,\ 2.0\}.   \end{cases}\) </li> <li>Example 5.2  \(\begin{cases}     ^{C}D_{0+}^{\alpha}y(x)+1.8 y(x)=0,\\     x\in \langle 0,2\rangle \quad \hbox{and} \quad \alpha=\{1.0,\ 0.8,\ 0.6,\ 0.4,\ 0.2\},\\     y(0)=1. \end{cases}\)</li> </ul>

opencc-by-4.0Jul 2021View details →

Indicative distribution map for Ecosystem Functional Group T7.5 Derived semi-natural pastures and old fields

This archive contains indicative distribution maps and profiles for T7.5 Derived semi-natural pastures and old fields, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith et al. (2020) for details. The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).

opencc-by-4.0Sep 2021View details →

GFN2-xTB structures of iCOM adsorbed on a cluster model of water molecules derived from a periodic model of crystalline ice

This dataset contains the atomic coordinates in the <a href="http://www.moldraw.unito.it/">.</a>xyz format of the GFN2-xTB optimized structures of 20 iCOMs adsorbed at the surface of  a cluster of 84 water molecules mimicking the periodic model of crystalline water icy grain as described by Ferrero, S.; Zamirri, L., Ceccarelli, C.; Witzel, A.; Rimola, A.; Ugliengo, P. ApJ, (2020) 904:11. For all considered structures we also provided a specific file in the Gaussian format with the computed harmonic frequencies. Each file can be easily converted in input for the variety of quantum mechanical programs, like VASP, QE, Gaussian 16 etc.

opencc-by-4.0Jan 2022View details →

De Obaldia et al. Differential mosquito attraction to humans is associated with skin-derived carboxylic acid levels

These supplementary files accompany the manuscript by De Obaldia et al. entitled "Differential mosquito attraction to humans is associated with skin-derived carboxylic acid levels." This includes all raw data in the paper, supplementary data, and instructions for the mosquito behavioral assays.   On January 2, 2023 we added one new data file and a .readme to explain changes between the original pre-print and the published peer-reviewed version of the paper https://pubmed.ncbi.nlm.nih.gov/36261039/

opencc-by-4.0Jan 2023View details →

Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.

An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018).  The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature.  The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here: <product_type>_<mission>_<mode>_<product>_<polarisation>_<starttime>_<endtime>_<orbitnumber>_<dataID>_<image>.fileextension For example: extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif

opencc-by-4.0Apr 2023View details →

Dataset containing binominal lexemes in Harakmbut (isolate, Peru), for "The derivational use of classifiers in Western Amazonia" and "When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut"

This is the dataset used, amongst others, in the paper: Van linden, An. Forthcoming. When the alienability contrast fails to surface in adnominal possession: Bound nouns in Harakmbut. Special Issue “Re-assessing the explanatory potential of alienability contrasts”, guest-edited by Françoise Rose & An Van linden. Linguistics – An Interdisciplinary Journal of the Language Sciences. [<a href="https://doi.org/10.1515/ling-2022-0039">https://doi.org/10.1515/ling-2022-0039</a>] For more details, see the ReadMe file.

opencc-by-4.0May 2023View details →

HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)

Hyperspectral Water Leaving Reflectance spectra (2988) measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the HYPSTAR® (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in Goyens et al.- Remote Sens. 2022 - 14(21)- 5607; https://doi.org/10.3390/rs14215607. <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e., <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated from the HYPSTAR® reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: Chlorophyll-a concentration estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: Suspended particulate matter estimated with the HYPSTAR® reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>

opencc-by-4.0Jul 2023View details →

Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes

Abstract Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks. Data Description This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold.  As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those. Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets. Paper & Code The original paper for this article can be accessed here: <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> To access the codes relevant for this project, please access the project GitHub Repos: <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> If using this work, please cite it based on the above paper, or using the following BibTex: <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre>   Data Access The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi Funding ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).

opencc-by-4.0Aug 2023View details →

Radar-derived storm characteristics and convective diagnostics associated with hourly maximum measured wind gusts around Australia

The data in this record describes various characteristics associated with hourly measured surface wind gusts across various locations in Australia, with these characteristics and data sources described below. This record provides all data used in the preparation of Brown et al. (2023a), except for lightning data that can be obtained from the <a href="https://wwlln.net/">World Wide Lightning Location Network archive</a> Record contents <ul> <li>gust_observations.zip Within this zip archive, a .csv file is provided for wind gust observations, along with associated storm statistics from radar, and convective diagnostics from a global reanalysis. These data are provided for each of the 20 radar domains listed in Brown et al. (2023a). The .csv files follow the structure: gust_observations_x.csv, where x is the identification number for each radar from the <a href="https://www.openradar.io/operational-network">Australian Unified Radar Archive</a>.  </li> <li>station_details.csv This file provides details on the automatic weather stations that measure the wind gusts, with station identifiers (column=Station_id) consistent between station_details.csv and gust_observations_x.csv.  </li> <li>Table1.pdf  Descriptions of convective diagnostics from reanalysis, that are provided in gust_observations_x.csv. This table has been extracted from the supplementary information of Brown et al. (2023a), and references in this table can be found therein.  </li> <li>radar_details.pdf  Taken from Table 1 from Brown et al. (2023a), showing the details of radars used here for storm statistics in gust_observations_x.csv.  </li> <li>Fig1.jpeg Taken from from Brown et al. (2023a), showing a map of the radar domains used here for storm statistics in gust_observations_x.csv.</li> </ul> Wind gust data Measured wind gusts here represent a 3-second average wind speed, at a height of 10 m above ground level. We also provide some derived quantities from the gust data (see table below). Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 204 automatic weather stations (station_details.csv), chosen to be within 100 km of a weather radar with sufficient archived data. These data are originally provided by the Bureau of Meteorology at 1-minute frequency, representing a maximum over a 1-minute interval, but are resampled in this record to hourly frequency, for comparisons with other hourly data below (see Brown et al. (2023a) for details of this resampling). Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Radar data Radar data is obtained by the <a href="https://www.openradar.io/">Australian Unified Radar Archive</a> (AURA), produced from operational weather radar within the Australian Bureau of Meteorology network. The level1b data used here is available from the AURA dataset on the Australian NCI under a CC4-BY-NC licence from <a href="https://dx.doi.org/10.25914/5f4c85732ee80">https://dx.doi.org/10.25914/5f4c85732ee80</a>. Various properties derived from radar reflectivity and Doppler velocity data is reported here in association with the wind gust observations. These properties are only reported if there is a storm object within 10 km and 10 minutes of the gust location (see Brown et al. (2023a) for storm object definition). Radar properties are described in the table below. Environmental data Various convective diagnostics are associated with wind gust observations, representing the convective environment and large-scale wind profile. These diagnostics are derived from a combination of pressure-level and surface-level ERA5 data (Hersbach et al. 2020), which is provided at hourly intervals on a 0.25-degree latitude-longitude grid, hosted on the Australian NCI (<a href="http://dx.doi.org/10.25914/5fb115b82e2ba">http:// dx.doi.org/10.25914/5fb115b82e2ba</a>). Details on these convective diagnostics are provided in Brown et al. (2023a), and in Table1.pdf as provided in this record. Column descriptions The following table provides descriptions of columns of gust_observations_x.csv <table> <thead> <tr> <th scope="col">Column name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>dt_utc</td> <td>Time of the measured wind gust, from the automatic weather station data (YYYY-MM-DD HH:MM:SS UTC)</td> </tr> <tr> <td>Station_id</td> <td>Identification number of the weather station that measured the gust. See station_details.csv for details of each station</td> </tr> <tr> <td>Wind_gust_observed</td> <td>The measured wind gust speed (m/s)</td> </tr> <tr> <td>Peak_to_mean_wind_gust_ratio</td> <td>Ratio of the measured wind gust to the 4-hour mean at that station (with the window centred on the gust time)</td> </tr> <tr> <td>SCW</td> <td>Is the measured gust a severe convective wind event? 0: Gust is either less than 25 m/s, does not have a storm object within 10 km, or has a peak-to-mean wind gust ratio less than 2. 1: Gust is greater than 25 m/s, has a storm object within 10 km, and has a peak-to-mean wind gust ratio greater than 2.</td> </tr> <tr> <td>Radar_id</td> <td>Radar identification number (see radar_details.pdf)</td> </tr> <tr> <td>Storm_speed</td> <td>Translational speed of the parent storm object (m/s). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Storm_angle</td> <td>Angle of parent storm object movement. In units of degrees from N. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Parent_storm_class</td> <td>The type of parent storm associated with a gust. Only defined if Storm_in10km=1. Possible types are: "Non-linear" "Linear" "Cellular" "Cell cluster" "Supercellular" "Embedded supercell" See Brown et al. (2023a) for classification details</td> </tr> <tr> <td>Storm_in10km</td> <td>Is there a radar-derived storm object within 10 km of the gust, observed no more than 10 minutes prior to the gust? 0: No 1: Yes See Brown et al. (2023a) for a definition of "storm object"</td> </tr> <tr> <td>Major_axis_length</td> <td>The length of the major axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Minor_axis_length</td> <td>The length of the minor axis of an ellipse fitted to the parent storm object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Local_reflectivity_maxima</td> <td>Number of local reflectivity maxima within the parent storm object. Only defined if Storm_in10km=1</td> </tr> <tr> <td>Maximum_storm_altitude</td> <td>The maximum height of the parent storm radar reflectivity object (km). Only defined if Storm_in10km=1</td> </tr> <tr> <td>Azimuthal_shear</td> <td>Azimuthal shear of the parent storm object derived from radar data (s-1 x 1000). Only defined if Storm_in10km=1. See Brown et al (2023a) for a discussion of azimuthal shear and processing applied to this quantity here.</td> </tr> <tr> <td>ERA5_time</td> <td>Time of the ERA5 environmental data that is associated with the measured gust, corresponding to the closest previous hour (YYYY-MM-DD HH:MM:SS UTC).</td> </tr> <tr> <td>ERA5_latitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of latitude</td> </tr> <tr> <td>ERA5_longitude</td> <td>Location of closest ERA5 (land) grid point to measured gust, in degrees of longitude</td> </tr> <tr> <td>Environmental_cluster</td> <td>Event type, based on statistical clustering of environmental data (Brown et al. 2023b) 0: Strong background wind cluster 1: Steep lapse rate cluster 2: High moisture cluster</td> </tr> <tr> <td>Umean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>U10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WindGust10</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>S06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EBWD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Umeanwindinf</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRHE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SRH06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DMI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_subcloud</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR_freezing</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR03</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>LR13</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMSI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>BDSD</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_wet</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ConvGust_dry</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>GUSTEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DmgWind_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WMPI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WINDEX</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DowndraftTemp</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ThetaeDiff</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>TEI</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>WNDG</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>DCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SCP_fixed</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERB</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SHERBE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>SWEAT</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>EffCS6</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>T_Totals</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>K_Index</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_CAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Eff_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MLCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>ML_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MUCAPE</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>MU_LCL</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean01</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> <tr> <td>Qmean06</td> <td>Convective diagnostic derived from ERA5 data. See Table1.pdf</td> </tr> </tbody> </table> References Brown, A., Dowdy, A., Lane, T. P., & Hitchcock, S. (2023b). Types of Severe Convective Wind Events in Eastern Australia. Monthly Weather Review, 151(2), 419–448. https://doi.org/10.1175/MWR-D-22-0096.1 Brown, A., A. Dowdy, T. P. Lane, & Hitchcock, S. (2023a). Long-term observational characteristics of different severe convective wind types around Australia. Wea. Forecasting, <a href="https://doi.org/10.1175/WAF-D-23-0069.1">https://doi.org/10.1175/WAF-D-23-0069.1</a>, in press. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 Global Reanalysis. Quarterly Journal of the Royal Meteorological Society, qj.3803. https://doi.org/10.1002/qj.3803

openCC (other)Jan 2025View details →

Groundwater-derived nutrient fluxes and offshore mixing rates along the New Jersey coast 21-23

Radium isotopes are natural tracers useful for studying the magnitude of groundwater discharge and the transport and fate of nutrients in the coastal ocean. We collected radium and nutrient samples from groundwater and surface waters along the southern New Jersey coast to calculate the flux of groundwater-derived nutrients and coastal mixing rates. These data serve as baselines for assessing future changes in the magnitude and quality of groundwater discharge driven by human activity and climate change.

openOpenOct 2025View details →

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022

This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.

Numerical summaries of vegetation indices and land surface temperature derived from remotely sensed imagery in Phoenix Area Social Survey (PASS) neighborhoods of central Arizona

This project calculates two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI), and land surface temperature (LST) from remotely sensed imagery. NDVI and SAVI are calculated from the 2010, 2013, 2015, and 2017 NAIP imagery (1m resolution). LST is calculated from Landsat 5 and 8 imagery (30m resolution) from summer months in 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Summary values are calculated for each of the aforementioned data resources for 2011 and 2017 Phoenix Area Social Survey (PASS) study area boundaries. Tabular summaries of the mean, median, minimum, maximum, and standard deviation of the NDVI, SAVI, and LST values for the 2011 and 2017 Phoenix Area Social Survey boundaries (45 and 12 neighborhoods, respectively) are provided. Javascript code used to process NDVI, SAVI, and LST imagery, and R code used to calculate numerical summaries of NDVI, SAVI, and LST in PASS neighborhoods are included with this dataset. Locations and areas of PASS study neighborhood boundaries and source imagery used to calculate these summaries are available through the Environmental Data Initiative - see resouce listing in the methods of this data set.

Normalized Difference Vegetation Index (NDVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Soil-Adjusted Vegetation Index (SAVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.

Soil-Adjusted Vegetation Index (SAVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region

This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.