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Data for "Misestimation of forest soil carbon and nitrogen stocks due to rock fragments: A case study of large number samples in a boreal forest watershed ecosystem of northeast China"
<p>Here are the data for "<span>Misestimation of forest soil carbon and nitrogen stocks due to rock fragments: A case study of large number samples in a boreal forest watershed ecosystem of northeast China</span>", using the format of"excel".</p>
Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p><strong>### Update 03/02/2025: the most up to date version of the processing pipeline presented here, also working on Linux, is available on github: https://github.com/fischer-fjd/GCA/tree/main, and a worked example with open data from the Dutch AHN surveys is available on Zenodo: https://zenodo.org/records/14722001 ###<br></strong></p> <p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It accompanies the article "Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists" (accepted in Methods in Ecology and Evolution on 25/08/2024). </p> <p>In the article, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to point cloud degradation (pulse density thinning, large scan angles, loss of higher-order returns) and how uncertainties and biases propagate to commonly used forest structure metrics. In addition, we provide a standardized processing pipeline in R to convert point clouds into CHMs. </p> <p>The main data source for this study are ALS point clouds from nine Australian research sites belonging to the Terrestrial Ecosystem Research Network (TERN, 5 km x 5 km extent each). The underlying data can be found here: https://portal.tern.org.au/metadata/TERN/4ff0b4c9-cfa0-4d09-9520-b5402adc583f. For one site (Robson Creek), we also used field data to assess the sensitivity of aboveground biomass estimates to ALS point cloud characteristics. Data are available here: https://portal.tern.org.au/metadata/supersite.174. </p> <p>To characterize climatic/environmental differences between sites, we used climatic data from the CHELSA/BIOCLIM+ climatology 1981-2010 (Brun et al. 2022: Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022; Karger et al. 2017: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122). </p> <p>We note that the enormous size of the full set of manipulated point clouds (original + thinned + individual flightlines: ~400 GB) and the derived raster products (~200 GB) by far exceeds limits on data storage in Zenodo. However, all analyses can be recreated from scratch from the openly available data and the R code in this repository. In addition, we include derived products for the nine study sites that allow to replicate results in the main text without any point cloud processing (CHMs and other rasters across thinned point clouds + summary statistics). </p> <p>The different data layers are:</p> <p><strong>01_rscripts.zip:</strong></p> <ul> <li>contains a sample script to test the processing pipeline (<em>test.processing.R</em>) as well as a collection of helper functions (<em>ALS_processing_helperfunctions_v40.R</em>); the script can be run directly after unzipping the folder, but an installation of LAStools (https://rapidlasso.de) is necessary (path_lastools = "PATH/TO/LASTOOLS/BIN"); we note that the script was developed on Windows PCs, its application with the recent Linux distribution of LAStools has not yet been tested</li> <li>contains the full set of scripts necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data (<em>create.CHMs.R)</em> as well as the overall robustness analysis (<em>analyze.CHMs.R</em>); to replicate the processing of the raw point clouds step by step, .laz files should be downloaded from the TERN repository (cf. citation above) and placed in a "data" folder, with subfolders for each site and with the same naming conventions as in this repository (e.g., "/data/Alice Mulga")</li> </ul> <p><strong>02_reference.zip</strong></p> <ul> <li>contains reference digital surface models (DSMs), canopy height models (CHMs) and digital terrain models (DTMs) for all nine TERN sites, based on the original ALS point clouds</li> <li>note that these reference layers are produced with the "CHMhighest" algorithm, which provides an easily interpretable canopy description as long as pulse densities are high (>= 20 shots per squaremetre)</li> </ul> <p><strong>03_climate.zip</strong></p> <ul> <li>contains site coordinates</li> <li>contains the climate layers from the CHELSA climatology (cf. citation above, only used to evaluate climatic ranges of sites)</li> </ul> <p><strong>04_robson_additional.zip</strong></p> <ul> <li>contains biomass estimates for Robson Creek</li> <li>contains shapefiles for large trees at Robson Creek (only used for visualization purposes)</li> </ul> <p><strong>05_downsampling_pulse_[Site name].zip</strong></p> <ul> <li>[Site name] is a stand-in for the nine TERN sites (e.g., "Alice Mulga.zip", "Credo.zip", etc.)</li> <li>contains the data necessary to reproduce results in the main text of the study, i.e. DSMs, CHMs, and DTMs for all nine TERN sites, and at different pulse density levels (from 16 down to 0.5 laser shots per squaremetre)</li> <li>also contains calculated summary statistics for each site</li> </ul> <p>All zip files should be extracted into the same folder, except for 05_downsampling_pulse_[Site name].zip which should all be moved to a subfolder called "downsampling_pulse".</p>
Data from: Optimal sampling interval for characterisation of the circadian rhythm of body temperature in homeothermic animals using periodogram and cosinor analysis
<p>Core body temperature (T<sub>c</sub>) is a critical aspect of homeostasis in birds and mammals and is increasingly used as a biomarker of the fitness of an animal to its environment. Periodogram and cosinor analysis can be used to estimate the characteristics of the circadian rhythm of T<sub>c</sub> from data obtained on loggers that have limited memory capacity and battery life. This data set contains five days of core body temperature, measured by loggers implanted into the abdominal cavity, in nine species of birds and mammals.</p>
Supplementary Data for Manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'
<p>This file contains the supplementary data for the manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'</p>
Data for: 10 years of fish species sampling in Rouge River, Michigan
<p>Community based citizen science has increased the scope of ecological data collection and monitoring. Despite its growing popularity, citizen science methods are rarely validated. Validation is important to ensure high quality data can be used in scientific studies, monitoring, and management. The Rouge River (Michigan, USA), an Environmental Protection Agency Area of Concern, is considered a highly degraded river, but has benefited from numerous restoration projects. These projects have improved abiotic conditions in the river, but improvements to the biotic communities have not been assessed. Friends of the Rouge, a non-profit, has collected fish assemblage data throughout the river network for 10 years by seining, a sampling method they selected due to concerns including cost, safety, and fit to the organization's volunteer-based monitoring program.</p> <p>We aimed to evaluate differences between sampling fish assemblages through seining performed by citizen scientists and the electrofishing method recommended for standardized assessments performed by fisheries professionals. We examined data from 48 sites across the Rouge River watershed where both sampling methods were implemented. We compared: a) species captured, b) the relationship between species richness and effort, c) diversity metrics used for standardized evaluation, and d) assemblage similarity between methods across the watershed.</p> <p>Our results showed that in the wadeable reaches of this urban river, electrofishing and seining were comparable. The majority of species captured within the reaches were shared across sampling methods, although community similarity was lowest and highest in small branches. Differences in species captured were mostly driven by rare and benthic species. Species accumulation curves were not significantly different at the watershed or subwatershed scales (except when non-wadeable reaches were included).</p> <p>Total species richness, the richness of species tolerant and intolerant to environmental degradation, and Procedure 51 scores used by Michigan agencies to assess the status of fish communities, sometimes differed among branches, but neither method was more effective overall at capturing fish diversity.</p> <p>Our work demonstrates how citizen science methods can be validated by comparison with standard methods. Validating citizen science data enhances utility for monitoring, assessment, and management decisions.</p>
HagesLab/Absorber_NN - Sample Training Data and Models
Open the record for dataset details and reuse information.
TU Delft SESANS data of reproducibility samples
<p>SESANS data measured on the SESANS instrument (at the Reactor Institute Delft at TU Delft, a reactor neutron source). The data are of SmartMembranes FlexiPor Membrane with 100 nm mean pore diameter (Delft_Flexipor.ses) and of a sheet of Papyex flexible graphite (Delft_Papyex.ses).</p> <p>Data are in the current (as of 14 November 2024) SESANS (or *.ses) format. This format includes metadata along with a header with four-column data (spin-echo length in Å, normalized scattering correlation function in Å^{-2} cm^{-1}, error in the normalized scattering correaltion function in the same units, and the neutron wavelength in Å).</p>
ORNL SESANS data of reproducibility samples
<p>SESANS data measured on the HB-2D instrument (at the High Flux Isotope Reactor HFIR at Oak Ridge National Laboratory ORNL, a reactor neutron source). The data are of SmartMembranes FlexiPor Membrane with 100 nm mean pore diameter (ORNL_Flexipor.ses) and of a sheet of Papyex flexible graphite (ORNL_Papyex.ses).</p> <p>Data are in the current (as of 14 November 2024) SESANS (or *.ses) format. This format includes metadata along with a header with four-column data (spin-echo length in Å, normalized scattering correlation function in Å^{-2} cm^{-1}, error in the normalized scattering correaltion function in the same units, and the neutron wavelength in Å).</p> <p>The detailed experimental setup can be found in F. Funama, C. M. Wolf, K. Weigandt, J. Shen, S. R. Parnell and F. Li, Spin echo small-angle neutron scattering using superconducting magnetic Wollaston prisms, Rev. Sci. Instrum., 2024, 95(7), 073709, DOI:10.1063/5.0217884.</p>
Sample data and algorithm implementation for ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern
<p>Sample data and algorithm implementation for the article </p> <div>Trelin, A., Kussauer, S., Weinbrenner, P., Clasen, A., David, R., Rimmbach, C., & Reinhard, F. (2024). ChiSCAT: Unsupervised Learning of Recurrent Cellular Micromotion Patterns from a Chaotic Speckle Pattern. <em>Nano Letters</em>, <em>24</em>(40), 12374-12381.</div>
Raw GC-ToF-MS and processed data from individuals sampled in allopatric and contact zones and MZmine 3.9.0 and Rstudio analysis
Open the record for dataset details and reuse information.
Data from: A densely sampled nuclear phylogenomic analysis of the coryphoid palms (Arecaceae − Coryphoideae)
<p><strong>Data from:</strong></p> <p>Wrisberg, O, Petoe, P, de Lima Ferreira, P, Bacon, CD, Barfod, AS, Bellot, S, Cano, Á, Couvreur, TLP, Dransfield, J, Henderson, A, Stauffer, F, Baker, WJ, Eiserhardt, WL (in review) <strong>A densely sampled nuclear phylogenomic analysis of the coryphoid palms (Arecaceae − Coryphoideae)</strong></p> <p>This repository is meant to provide the most important data outputs produced by the Pipeline created for this project. The analysis pipeline is located on github (https://github.com/pebgroup/coryphoideae_species_tree). Raw data can be found on the NCBI Sequence Read Archive. </p> <p>The data folder is divided into the following subfolders:</p> <p><strong>01_unaligned_sequences_per_specimen</strong></p> <p>This folder contains the unaligned sequences for each specimen. The sequences are named after the specimen number.</p> <p><strong>02_unaligned_sequences_per_gene</strong></p> <p>This folder contains the unaligned sequences for each gene. The sequences are named after the gene name.</p> <p><strong>03_aligned_sequences_per_gene</strong></p> <p>This folder contains the sequences aligned by MAFFT for each gene. The sequences are named after the gene name.</p> <p><strong>04_gene_trees</strong></p> <p>This folder contains the gene trees for each gene. The trees are named after the gene name. The subfolder <strong>subset_single_copy_gene_trees</strong> contains copies of the gene trees for the single copy genes.</p> <p><strong>05_species_trees</strong></p> <p>This folder contains the species trees. The subfolder <strong>all_genes</strong> contains the species trees based on all genes, while the subfolder <strong>single_copy_genes</strong> contains only the species trees based on the single copy genes.</p> <p><strong>06_supporting_information</strong></p> <p>This folder contains a list which contains the associations between tip names and specimen numbers and a list of the single-copy genes.</p>
Sentinel-2 Sample Data
<p>This repository contains 5 Sentinel-2 Level-2A (12 bands) images that are part of the BigEarthNet Dataset (Sumbul et al. 2019, https://bigearth.net/). The images in this dataset focus on coastal areas.</p> <p>The image data for each scene and band were upscaled to a common ground sample distance of 10m per pixel using linear interpolation. Furthermore, all bands of each scence were combined into a single NumPy array and stored into separate .npy binary files. Data processing was performed by Linus Scheibenreif, University of St. Gallen.</p> <p>The data can be easily read in with Python using the following code snippet:</p> <p><code>import os</code><br><code>import numpy as np</code></p> <p><code>data = []</code><br><code>for filename in os.listdir('data/'):</code><br><code> if filename.endswith('.npy'):</code><br><code> data.append(np.load(open(os.path.join('data', filename), 'rb'),</code><br><code> allow_pickle=True))</code><br><code>data = np.array(data)</code></p> <p>This repository also contains the file coastal_labels.json, which contains polygons for labels grassland, forest, water and sand, using the YOLO format.</p> <p>This dataset is provided mainly for teaching purposes under the Creative Commons Attribution 4.0 International licence. BigEarthNet data are provided under the Community Data License Agreement (Permissive, Version 1.0).</p> <p>Michael Mommert, Stuttgart University of Applied Sciences, 2025-03-07</p>
UVAE toy data (flow cytometry sample)
<p>A sample of flow cytometry data containing 3 panels of 3 batches with artificial batch effects.</p>
Sample based prevalence data complementing the European Union One Health 2020 Zoonoses Report - Croatia
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Western Antarctic marine mammal and seabird distance sampling data
<p>These datasets are:</p> <p>1) Raw (MS Access) IFAW Logger2010 tables (<a href="http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/">http://www.marineconservationresearch.co.uk/downloads/logger-2000-rainbowclick-software-downloads/</a>) and</p> <p>2) .RData objects preprocessed by the R package LoggeR (<a href="https://github.com/embiuw/LoggeR">https://github.com/embiuw/LoggeR</a>), for distance sampling of marine mammals and seabirds from two ships of opportunity along the Western Antarctic Peninsula, Drake Passage and Scotia Sea during the 2019 - 2020 austral summer. The ships were the MS Fram and MS Midnatsol of the Hurtigruten fleet, with data collected from the start of December 2019 until late January 2020. </p>
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants. in Phylogenetic approach for identification and life cycles of Puccinia (Pucciniaceae) species on Poaceae from northeastern China
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants.
out-of-sample data for CNRM/CNRS
<p>Projections from the CMIP5 ensemble constrained by CMIP6 random realizations.</p>
Data for collected samples and mating experiment of Acilius Japonicus
<p>Previous studies have predicted that antagonistic intraspecific evolution of sexually dimorphic characters causing rapid speciation can be driven by demographic history and environmental variations. However, researchers have rarely examined this issue in the wild. Here, we examined intraspecific evolution of sexually dimorphic characters and its driving force by using a diving beetle, <em>Acilius japonicus</em>, which has very marked sexually dimorphic characters. Males with wider big suction cups could copulate with females with a higher success rate, whereas the mating durations of females with more hairs on their pronota were shorter. Females in a region with greater interpopulation genetic differentiation had more pronotal hairs. Considering that a previous study showed that less continuity among populations leads to a higher female cost of mating, this result suggests a greater female cost of mating in this region. Females at warmer sites also had more pronotal hairs. In light of the increase in O<sub>2</sub> consumption in warmer water, our result suggests that more pronotal hairs in females at warmer sites have been maintained to prevent prolonged underwater mating at higher O<sub>2</sub> demand. These findings suggest that demographic history and temperature can direct the evolution of sexually dimorphic characters related to sexual conflict in females.</p>
Geochemistry and Geochronology data for Great Slave Lake shear zone supracrustal samples
<p>Supplementary tables include bulk compositions used for phase equilibria modelling (Table S1), garnet trace-element data (Tables S2-S3), garnet Lu-Hf isotopic data (Table S4), monazite (Table S5) and zircon (Table S6) U-Pb and trace-element data, and laser ablation-inductively coupled plasma-mass spectrometry instrument settings and metadata (Table S7).</p>
Data from: A new lineage of Galapagos giant tortoises identified from museum samples
<p>The Galapagos Archipelago is recognized as a natural laboratory for studying evolutionary processes. San Cristóbal was one of the first islands colonized by tortoises, which radiated from there across the archipelago to inhabit 10 islands. Here, we sequenced the mitochondrial control region from six historical giant tortoises from San Cristóbal (five long deceased individuals found in a cave and one found alive during an expedition in 1906) and discovered that the five from the cave are from a clade that is distinct among known Galapagos giant tortoises but closely related to the species from Española and Pinta Islands. The haplotype individual collected alive in 1906 is in the same clade as the haplotype in the contemporary population. To search for traces of a second lineage in the contemporary population on San Cristóbal, we closely examined the population by sequencing the mitochondrial control region for 129 individuals and genotyping 70 of these for both 21 microsatellite loci and >12 000 genome-wide single nucleotide polymorphisms [SNPs]. The dataset archived here consists of a VCF file of the SNPs genotyped through ddRAD and a structure file of the 21 microsatellites with the genotypes for the same 64 individuals in each. Only a single mitochondrial haplotype was found, with no evidence to suggest substructure based on the nuclear markers.</p>
ScienceDex guides
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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.