Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data

In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies. This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166. We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.

opencc-zeroApr 2024View details →

The role of growing degree-days in explaining moth species distributions at broad scales

Growing degree-days (GDD), an estimate of an organism's growing season length, has been shown to be an important predictor of Lepidopteran species' distributions and could be influencing Lepidopteran range shifts to climate change. Yet, one understudied simplification in this literature is that the same thermal threshold is used in the calculations of GDD for all species instead of a species-specific threshold. By characterizing the phenological process influenced by climate, a species-specific estimate of GDD should improve the accuracy of species distribution models (SDMs). To test this hypothesis, we use published lab-estimated thermal thresholds and modeled the current geographic distribution of 30 moth species native to North America. We found that the predictive performance of models based on a species-specific estimate of GDD was indistinguishable from models based on a standard estimate of GDD. This is likely because GDD was not an important predictor of these species' distributions. Our findings suggest that lab-estimated thermal thresholds may not always scale up to be predictive at broad scales and that more work is needed to leverage the data from lab experiments into SDMs to accurately predict species' range shifts in response to climate change.

opencc-zeroApr 2024View details →

The scaling of metabolic traits differs among larvae and juvenile colonies of scleractinian corals

Body size profoundly affects organism fitness and ecosystem dynamics through the scaling of physiological traits. This study tests for variation in metabolic scaling and its potential drivers among corals differing in life history strategies and taxonomic identity. Data were compiled from published sources and augmented with empirical measurements of corals in Moorea, French Polynesia. The data compilation revealed metabolic isometry in broadcasted larvae, but size-independent metabolism in brooded larvae; empirical measures of Pocillopora acuta larvae also supported size-independent metabolism in brooded coral larvae. In contrast, for juvenile colonies (i.e., 1–4 cm diameter), metabolic scaling was isometric for Pocillopora spp. and negatively allometric for Porites spp. The scaling of biomass with surface area was isometric for Pocillopora spp., but positively allometric for Porites spp., suggesting the surface area:biomass ratio mediates metabolic scaling in these corals. The scaling of tissue biomass and metabolism was not affected by light treatment (i.e., either natural photoperiods or constant darkness) in both juvenile taxa. However, biomass was reduced by 9–15% in the juvenile corals from the light treatments and this coincided with higher metabolic scaling exponents, thus supporting the causal role of biomass in driving variation in scaling. This study shows that metabolic scaling is plastic in the early life stages of corals, with intrinsic differences between life history strategy (i.e., brooded and broadcasted larvae) and taxa (i.e., Pocillopora spp. and Porites spp.), and acquired differences attributed to changes in area-normalized biomass.

opencc-by-4.0Feb 2024View details →

Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"

Data and code for  "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand" All R code used for the analysis is provided in the folder code.  Data and intermediate results are provided or stored in the folders data and tmp_data. All results including figures will be stored in the folder results.  R version: 4.3.1 To carry out the entire analysis the code should be run in the provided order: 1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and  produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)   2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3).  Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip. 3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation), output are Fig. 4, Fig. S1, Tab. 1. 4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2.  5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type.

opencc-by-4.0Dec 2020View details →

FIGURE 3. SHSU 1-236, distal rib fragment. Scale bar equals 2 in First fossil manatees in Texas, USA: Trichechus manatus bakerorum from Pleistocene beach deposits along the Gulf of Mexico

FIGURE 3. SHSU 1-236, distal rib fragment. Scale bar equals 2 cm.

opencc-by-4.0Apr 2024View details →

Data files for analysis of scaling relations between relative and absolute humidity and rainfall extremes

data belonging to: https://github.com/mister-superCC/CCscaling-Evaluation Contains: Dutch observarions in netcdf: KNMI_20201124_hourly.nc Model data as an R object file: DATA_SCALING_PRINCIPLES.tar.gz Processed model and observational data (including SFR) in: data_hourly_bootstrap.tar.gz and data_hourly_bootstrap_abs.tar.gz

opencc-by-4.0Apr 2024View details →

Unsupervised detection of Large-scale Weather Patterns in the Northern Hemisphere via Markov State Modelling: from Blockings to Teleconnections

Data Source This dataset is derived from the NCEP-NCAR Reanalysis 1 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href="https://psl.noaa.gov/">https://psl.noaa.gov</a>., a robust atmospheric dataset that includes a wide range of climatic measurements essential for comprehensive climate analysis. The original data can be accessed at the NOAA Physical Sciences Laboratory website:  https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html.

Data for "Testing for variation in photoperiodic plasticity in a butterfly: inconsistent effects of circadian genes between geographic scales" (Ecology and Evolution, accepted manuscript)

Raw data and analysis scripts belonging to Testing for variation in photoperiodic plasticity in a butterfly: inconsistent effects of circadian genes between geographic scales (Ecology and Evolution, accepted manuscript). Includes data from a photoperiodic assay with larvae of the speckled wood butterfly, Pararge aegeria, as well as a small bioinformatic analysis of SNP variation in two circadian candidate genes. List of files: <table> <tbody> <tr> <td>statistics_and_figures.R</td> <td>Statistical analysis of phenotyping experiment; drawing figures</td> </tr> <tr> <td>variant_calling.sh</td> <td>Shell script for mapping sequencing reads; calling and tabulating SNPs</td> </tr> <tr> <td>experiment_diapause.txt</td> <td>Data from phenotyping experiment, for analysis of diapause induction</td> </tr> <tr> <td>experiment_larval</td> <td>Data from phenotyping experiment, for analysis of larval development</td> </tr> <tr> <td>snptable_timeless</td> <td>Tabulated allele frequencies for exonic SNPs in timeless</td> </tr> <tr> <td>snptable_period</td> <td>Tabulated allele frequencies for exonic SNPs in period</td> </tr> </tbody> </table>

Swarm-C Neutral Density Data for Large-Scale Wave (LSW) Structures

Open the record for dataset details and reuse information.

Comprehensive large-scale datasets for 26 viral families for fine-tuning BERT-infect models

These datasets were constructed in the paper "Hidden Challenges in Evaluating Spillover Risk of Zoonotic Viruses using Machine Learning Models" (doi: https://doi.org/10.1101/2024.04.25.591033). The details were also described in the git-hub (https://github.com/Junna-Kawasaki/BERT-infect_2024). <ul> <li>The compressed files, such as ${virus}.tar.xz, contain fasta and genbank files.</li> </ul>

Urban warming signal across scales

Data for urban signal on land surface temperature (LST) and air temperature (AT) at country, climate zone, and select regional scales from different sources. The data are divided into the following directories: <ul> <li>LST: LST data with and without urban pixels at country scale from different LST dproducts and based on different land cover data by year.</li> <li>AT: AT data with and without urban pixels at country scale based on the default ESA CCI land cover product by year.</li> <li>LU: Urban area by country based on different products by country and year.</li> <li>MISC: Other variables calculated with and without urban pixels at country scale based on the default ESA CCI land cover product by year.</li> <li>FUT: Future projections of urban area by country, year, and SSP.</li> <li>REG: LST data with and without urban pixels for select regions from different LST dproducts and based on different land cover data by year.</li> <li>CLIM: LST data with and without urban pixels and urban area for climate zones based on the default ESA CCI land cover product and MODIS LST by year.</li> <li>PREIND: Pre-industrial estimates of urban area by country based on the latest HYDE dataset.</li> </ul> In all cases, data for the European and Asian portions of Russia are separately calculated and adjusted to get continental-scale estimates.

Data from: Scaling of leaf area with biomass in trees reconsidered: Constant metabolically active sapwood volume per unit leaf area with height growth

Hypoallometric (slope<1) scaling between metabolic rate and body mass is often regarded as near-universal across organisms. However, there are compelling reasons to question hypoallometric scaling in woody plants, where metabolic rate=leaf area. This leaf area must provide carbon to the metabolically active sapwood volume (VMASW). Within populations of a species, variants in which VMASW increases per unit leaf area with height growth (e.g. ⅔ or ¾ scaling) would have proportionally less carbon for growth and reproduction as they grow taller. Therefore, selection should favor individuals in which, as they grow taller, leaf area scales isometrically with shoot VMASW (slope=1). Using tetrazolium staining, we measured total VMASW and total leaf area (LAtot) across 22 individuals of Ricinus communis and confirmed that leaf area scales isometrically with VMASW, and that VMASW is much smaller than total sapwood volume. With the potential of the LAtot-VMASW relationship to shape factors as diverse as the crown area-stem diameter relationship, conduit diameter scaling, reproductive output, and drought-induced mortality, our work suggests that the notion that sapwood increases per unit leaf area with height growth requires revision.

Continental-scale associations of Arabidopsis thaliana phyllosphere members with host genotype and drought

Plants are colonized by distinct pathogenic and commensal microbiomes across different regions of the globe, but the factors driving their geographic variation are largely unknown. Using 16S rDNA and shotgun sequencing, we characterized the associations of the Arabidopsis thaliana leaf microbiome with host genetics and climate variables from 267 populations in the species’ native range across Europe. Comparing the distribution of the 575 major bacterial amplicon variants (phylotypes), we discovered that microbiome composition in A. thaliana segregates along a latitudinal gradient. The latitudinal clines in microbiome composition are predicted by metrics of drought, but also by the spatial genetics of the host. To validate the relative effects of drought and host genotype we conducted a common garden field study, finding 10% of the core bacteria to be affected directly by drought, and 20% to be affected by host genetic associations with drought. These data provide a valuable resource for the plant microbiome field, with the identified associations suggesting that drought can directly and indirectly shape genetic variation in A. thaliana via the leaf microbiome.

Data and analysis scripts for the submission "Seamlessly Scaling Applications with DAPHNE"

To regenerate the plots: You will need to install `R` (4.3.2) and the `tidyverse` package (2.0.0) We give a Nix flake that captures this environment (Install Nix: https://nixos.org/download/ and activate the flake feature: https://nixos.wiki/wiki/Flakes#Other_Distros.2C_without_Home-Manager) With Nix: `nix develop --command Rscript analysis_compas.R` Without Nix: `Rscript analysis_compas.R`

An evaluation of cloud-precipitation structures in mixed-phase stratocumuli over the southern ocean in kilometer-scale ICON simulations during CAPRICORN

The repository contains ICON simulated outputs (Cntrl_ICON.nc.gz, No_Gr_ICON.nc.gz, No_Ice_ICON.nc.gz, 100m_Vert_ICON.nc.gz, and 50m_Vert_ICON.nc.gz) and two km mean and ensemble forward simulated results (Cntrl_PAMTRA.gz, No_Gr_PAMTRA.gz, No_Ice_PAMTRA.gz, 100m_Vert_PAMTRA.gz, and 50m_Vert_PAMTRA.gz) for the two day period (00:00 UTC on March 26, 2016 to 00:00 UTC on March 28, 2016). The prefix in the filenames corresponds to the experiments described in the manuscript.

Data from: Pilot-scale H2S and swine odor removal system using commercially available biochar

This is digital research data corresponding to a published manuscript in "Pilot-scale H2S and swine odor removal system using commercially available biochar" Agronomy 2021, 11, 1611. Dataset may be assessed via the included link at the Dryad data repository. Although biochars made in laboratory seem to remove H2S and odorous compounds effectively, very few studies are available for commercial biochars. This study evaluated the efficacy of a commercial biochar (CBC) for removing H2S and odorous volatile organic compounds (VOCs).

Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila

The atypical cadherins Fat and Dachsous (Ds) signal through the Hippo pathway to regulate growth of numerous organs, including the Drosophila wing. Here, we find that Ds-Fat signaling tunes a unique feature of cell proliferation found to control the rate of wing growth. The duration of the cell cycle increases in direct proportion to the size of the wing, leading to linear rather than exponential growth. Ds-Fat signaling enhances the rate at which the cell cycle lengthens with wing size, thus diminishing the linear rate of wing growth. We show that this results in a complex but stereotyped relative scaling of wing growth with body growth in Drosophila. Finally, we examine the dynamics of Fat and Ds protein distribution in the wing, observing graded distributions that change during growth. However, the significance of these dynamics is unclear since perturbations in expression have negligible impact on wing growth.

opencc-by-4.0Apr 2024View details →

MobileWell400+: A Large-Scale Multivariate Longitudinal Mobile Dataset for Investigating Individual and Collective Well-Being

This study engaged 409 participants over a period spanning from July 10 to August 8, 2023, ensuring representation across various demographic factors: 221 females, 186 males, 2 non-binary, year of birth between 1951 and 2005, with varied annual incomes and from 15 Spanish regions. The MobileWell400+ dataset, openly accessible, encompasses a wide array of data collected via the participants' mobile phone, including demographic, emotional, social, behavioral, and well-being data. Methodologically, the project presents a promising avenue for uncovering new social, behavioral, and emotional indicators, supplementing existing literature. Notably, artificial intelligence is considered to be instrumental in analysing these data, discerning patterns, and forecasting trends, thereby advancing our comprehension of individual and population well-being. Ethical standards were upheld, with participants providing informed consent.  The following is a non-exhaustive list of collected data: <ul> <li>Data continuously collected through the participants' smartphone sensors: physical activity (resting, walking, driving, cycling, etc.), name of detected WiFi networks, connectivity type (WiFi, mobile, none), ambient light, ambient noise, and status of the device screen (on, off, locked, unlocked).</li> <li>Data corresponding to an initial survey prompted via the smartphone, with information related to demographic data, effects and COVID vaccination, average hours of physical activity, and answers to a series of questions to measure mental health, many of them taken from internationally recognised psychological and well-being scales (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception.</li> <li>Data corresponding to daily surveys prompted via the smartphone, where variables related to mood (valence, activation, energy and emotional events) and social interaction (quantity and quality) are measured.</li> <li>Data corresponding to weekly surveys prompted via the smartphone, where information on overall health, hours of physical activity per week, lonileness, and questions related to well-being are asked.</li> <li>Data corresponding to an final survey prompted via the smartphone, consisting of similar questions to the ones asked in the initial survey, namely psychological and well-being items (PANAS, PHQ, GAD, BRS and AAQ), social isolation (TILS) and economic inequality perception questions.</li> </ul> For a more detailed description of the study please refer to MobileWell400+StudyDescription.pdf. For a more detailed description of the collected data, variables and data files please refer to MobileWell400+FilesDescription.pdf.

Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks

<div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <div>(*) Both authors contributed equally. </div> <h2>Update</h2> <div>(04/09/2024): Citation is updated. We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version.  The EXN network has been included in this version, so the total number of processed networks is 11. For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,     AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},     TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},     JOURNAL = {Engineering Proceedings},     VOLUME = {69},     YEAR = {2024},     NUMBER = {1},     ARTICLE-NUMBER = {50},     URL = {https://www.mdpi.com/2673-4591/69/1/50},     ISSN = {2673-4591},     DOI = {10.3390/engproc2024069050} }</blockquote> </div>

Data from: Lianas associated with continued forest biomass losses following large-scale disturbances

Lianas are important to rainforest ecosystems but often impede tree growth and increase tree mortality and stem damage after disturbances that favour their growth. Understanding how lianas affect biomass recovery and rates of carbon sequestration following disturbance is therefore of crucial importance. In this study, we determine how a tropical forest recovers biomass following a large-scale disturbance and test how this varies with liana dominance and stem damage. We use remote sensing methods to develop a model, validated by field data from 40 20 m x 20 m vegetation plots, to measure change in tree above-ground biomass eight years after Tropical Cyclone Yasi damaged logged forests in the Australian Wet Tropics. We related tree biomass changes to field measures of current liana dominance over trees, expressed as liana: tree basal area ratio, and assessed how these measures related to tree stem damage. Biomass declined in 34 of the 40 plots during the eight years post-disturbance, with loss rates and proportions of damaged tree stems increasing with the liana: tree ratio. From spatial upscaling, we found a net loss in biomass across the study landscape over the same period. Our results show that, following disturbances, lianas not only limit tree biomass recovery but are also associated with further biomass declines, most likely through their contribution to stem damage and delayed mortality. Furthermore, our finding of net biomass loss across the landscape since the cyclone shows that, post-disturbance, rainforests can act as a carbon source with consequences for the global carbon sink.