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1,773 results for “Supplementary data,”
Supplementary Information for "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" - Experimental Data
<p>This is the external Supplementary Information for our publication "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase", freely available as a preprint from <em>ChemRxiv </em>(<a href="https://doi.org/10.26434/chemrxiv-2024-2lmv8-v2">https://doi.org/10.26434/chemrxiv-2024-2lmv8-v2</a>) and as a peer-reviewed publication from <em>Angewandte Chemie</em> (<a href="https://doi.org/10.1002/anie.202412597">https://doi.org/10.1002/anie.202412597</a>).</p> <p>The .zip files contain the raw data and metadata for all items (supplementary and main text) as well as the calculation results underlying each figure panel. This includes UV, fluorescence and NMR data. The zip files below also contain the ChimeraX session used to prepare all figure panels, the results of crystallization screens and reports on all MPLC runs used for the purification of pyrophosphate substrates. The <strong>NMR data for the synthesized compounds</strong> are described in the un-zipped metadata sheet which is separately stored below.</p> <p>The <strong>computational results</strong> contributed by Sangwar Wadtey Oung are stored in a separate zenodo entry (<a href="../doi/10.5281/zenodo.10635216">https://zenodo.org/doi/10.5281/zenodo.10635216</a>).</p> <p>This work was enabled by our previous studies on continuous reaction monitoring of phosphate-releasing transformations (<a href="https://doi.org/10.1021/acs.analchem.1c05356">https://doi.org/10.1021/acs.analchem.1c05356</a>), which itself builds on principles of spectral unmixing (<a href="https://doi.org/10.1002/cbic.202000204">https://doi.org/10.1002/cbic.202000204</a>), isosbestic normalization (<a href="https://doi.org/10.1002/cbic.202200744">https://doi.org/10.1002/cbic.202200744</a>) and thermodynamic reaction control in enzymatic equilibrium systems (<a href="https://doi.org/10.1021/acscatal.1c02589">https://doi.org/10.1021/acscatal.1c02589</a> & <a href="https://doi.org/10.1002/anie.202218492">https://doi.org/10.1002/anie.202218492</a> & <a href="https://doi.org/10.1002/adsc.201901230">https://doi.org/10.1002/adsc.201901230</a>).</p>
The Femern-project: a large-scale excavation of a Stone Age landscape - supplementary data
<p>This dataset contains all radiocarbon dates from the Femern project.</p> <p>Please cite the dataset as: </p> <p>Måge, B.T., Groß, D., Kanstrup, M. 2023. The Femern-project: a large-scale excavation of a Stone Age landscape. In: Groß, D. and Rothstein, M.: Changing Identity in a Changing World. Archaeological Studies on Human Interaction in Northern Europe around 4000 cal BC. Leiden: Sidestone, supplementary material.</p> <p>19.02.2024: Dataset updated: Wrong species ID in original dataset for AAR-27426</p>
Supplementary data to Frasson et al. 2024
<p>Supplementary data and code for Frasson et al. 2024 accepted for publication in Solid Earth. The data consist of full snapshots of the geoids, topography, CMB heat flux, and composition at the CMB for the six cases of the study; the PCA outputs for the six cases; and movies showing the time evolutions of the outputs in the mantle convection models. The scripts can be ran on the snapshots to compute the principal component analysis (PCA) and the true polar wander (TPW).</p> <p> </p> <p>The full snapshots are given in HDF5 format, with one file per time step. They can be found in the archives MF.zip and MC.zip for the MF model and the MC model respectively. The files contain:</p> <ul> <li>The fields in the physical space. The first coordinate is the latitude, the second coordinate is the longitude (dataset "data").</li> <li>The latitudes and longitudes associated with the fields (datasets "latitude" and "longitude").</li> <li>Some information regarding the snapshots (attributes "Time (Myr)", "Model", "Case", "Correction", and "Unit").</li> </ul> <p>The files are called "name_xxxx.h5", where xxxx stands for the time in Myr before the end of the simulation.The names of the files are:</p> <ul> <li>qcmb: CMB heat flux</li> <li>geoid: Total geoid</li> <li>geoid_nolvv: No LVVs geoid</li> <li>topo: Topography</li> <li>prim: Composition at the CMB</li> </ul> <p> </p> <p>The PCA outputs are given in HDF5 format. Each file has an "outputs" and a "grid" group. The "outputs" section gives:</p> <ul> <li>The average heat flux pattern. The first coordinate is the latitude, the second coordinate is the longitude (dataset "average").</li> <li>The list of patterns for each PCA component in physical space. The first coordinate is the latitude, the second coordinate is the longitude (dataset "pattern").</li> <li>The time-dependent weight of the components. The first coordinate is the time, the second coordinate is the component number (dataset "weights").</li> <li>The singular values of the components (dataset "singular values").</li> <li>The time in Myr (dataset "Time").</li> <li>The component numbers (dataset "components").</li> </ul> <p>The "grid" group gives the latitudes (dataset "latitude") and the longitudes (dataset "longitude") associated with the patterns.</p> <p> </p> <p>Description of the movies:</p> <ul> <li>outputs_MF: As in Fig. 2 in Frasson et al. 2024.</li> <li>outputs_MC: As in Fig. 3 in Frasson et al. 2024.</li> <li>qcmb_MF: CMB heat flux in cases MF0, MF1, MF2, and MF* in a Mollweide projection. Black lines delineate the edges of basal chemical piles.</li> <li>qcmb_MC: CMB heat flux in cases MC0 and MC1 in a Mollweide projection. Black lines delineate the edges of basal chemical piles.</li> </ul> <p> </p> <p>Description of the scripts</p> <ul> <li>PCA.py: Computes the PCA of the field stored in the path provided as argument for the given model.</li> <li>inertia.py: Computes the successive positions of the inertia axis. This script includes functions to rotate the outputs according to the positions of the inertia axis.</li> </ul>
Data and supplementary material used for Soundscapes to Landscapes soundscape mapping
<p>This repository contains supporting data products to enable the soundscape mapping outlined in the associated publication (DOI forthcoming). Data were used to extract acoustic recording location environmental data for training random forest models to spatially predict 2021 ecoacoustic metrics. The accompanying code will be linked to the GitHub repository. Files include:</p> <p>Data:</p> <ul> <li>clustered_fold_k10.rsd: indices of the model data used if geoCV approach</li> <li>extracted_predictors_vif3.csv: site-specific predictor values extracted from predictors_annual_20230223.tif</li> <li>final_predictors_vif3.csv: a two column table summarizing the VIF selected predictors</li> <li>final_sites_2017-2021.csv: the list of 1,195 potential sites</li> <li>predictor_sprmn_corr.csv: correlation matrix for predictors in model data</li> <li>predictors_annual_20230223.tif: all predictors </li> <li>response_df_200623.csv: site level ecoacoustic metrics</li> </ul> <p>Results:</p> <ul> <li>map_correlations.tar: pairwise response map correlations</li> <li>pdps.tar: partial dependence plot data</li> <li>performance.tar: model performance summaries</li> <li>predictions_maps.tar: final median and IQR model prediction surfaces</li> <li>variable_importance.tar: summaries for variable importance analyses</li> </ul> <p>Contact Colin Quinn at cq73@nau.edu for questions related to this repository or the underlying work. Original wav recordings are expected to be made publicly available on the NASA DAACs in the near future. </p>
Supplementary data for "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity"
<h3>This dataset is supplementary to the article "Effect of Uncertainty in Water Vapor Continuum Absorption on CO2 Forcing, Longwave Feedback, and Climate Sensitivity".</h3> <h3>spectral_olr.nc</h3> <p>This file contains the spectral outgoing longwave radiation (OLR) calculated using the line-by-line radiative transfer model ARTS and the radiative-convective equilibrium model konrad. It contains spectral OLR for surface temperatures from 270K to 330K for different strengths of the water vapor continuum absorption.</p> <h3>opacity_emission_level.py</h3> <p>This file also contains the spectrally resolved optical depth and the emission level of outgoing longwave radiation for the considered absorption species (H2O lines, H2O continuum, H2O self continuum, H2O foreign continuum, CO2, N2, and O2).</p> <h3>continuum_reference_conditions.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the single-constraint experiment.</p> <h3>continuum_all_profiles.nc</h3> <p>This file contains the reference continuum absorption coefficients that were used to calculate the adjustment to the foreign continuum for the general-constraint experiment.</p> <h3>modified_continuum_input_files_single_constraint.zip and modified_continuum_input_files_general_constraint.zip</h3> <p>These files contain the modified continuum data files used for the implementation of the MT_CKD continuum model in the line-by-line model ARTS for the single-constraint and general-constraint experiments, respectively.</p> <h3>tau_column.nc and tau_profile.nc</h3> <p>These files contain separately for each absorption species the vertically integrated opacity spectra, and the opacity profiles at two selected wavenumbers.</p> <p> </p>
Savanna-like Mammalian Community in the Mid-Miocene of Northern China--Supplementary Data
<p><strong><span>Supplementary Data 1:</span></strong><span> Stable Carbon and Oxygen isotopes data of Junggar Basin and Tongxin region, China.</span></p> <p><strong><span>Supplementary Data 2:</span></strong><span> Teeth hypsodonty of herbivorous mammals from China and Europe, including Sandelzhausen, Steinheim, Sansan, Madrid Basin, Qaidam Basin, Tunggur region, Baode, Lantian, Junggar Basin and Tongxin region.</span></p> <p><strong><span>Supplementary Data 3: </span></strong><span>Body mass of herbivorous mammals from different locations in China. </span></p>
Supplementary data: Winter cover cropping: Effect on soybean and synergistic implications on soil microbiome
<p>Supplementary data: (i) Agronomic and quality data of soybean (2 varieties) grown in 2 years (2020 & 2021) in two management systems (organic & low-input) with different cover crops; (ii) Soil microbiome analysis of the soybean field trials.</p>
Supplementary data for: On the Neotropical spider genus Ciniflella Mello-Leitão, 1921 (Araneae: Zoropsidae, Tengellinae)
<p>Phylogenetic datasets, trees and supplementary figures.</p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Supplementary micro-X-ray Fluorescence data for: "On the possible contribution of meteoritic metal to some Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analogue objects"
<p>This repository contains the micro X-Ray Fluorescence (microXRF) results described within the manuscript titled “On the possible contribution of meteoritic metal to Ni-rich Indonesian kris daggers: Comparing original daggers and newly forged analog objects” submitted to the Meteoritics and Plantetary Science (MAPS) journal by Brandstätter et al. The manuscript describes two types of microXRF results: Semi-quantitative maps and quantified line scan results. The README file contains a detailed overview of which files contain which data.</p>
Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts".
<p>Supplementary data for Willemsen et al., 2024 "Novel high-quality amoeba genomes reveal widespread codon usage mismatch between giant viruses and their hosts". The data set consists of five folders: “Codon_usage_amoebae_and_viruses”, "Genome_annotations_amoebae", "Phylogenetic_trees_18S_amoebae", “Phylogenomic_trees_amoebae”, and "Viral_integration_detection_amoebae". The “Codon_usage_amoebae_and_viruses” folder contains for each amoeba host the calculated codon usage tables in the subfolder "codon_usage_table_host", the calculated codon usage preferences using different scores in the subfolder "codon_usage_scores_host", and the calculated codon usage preferences of giant viruses versus each host in the subfolder "codon_usage_scores_viruses_vs_host". The giant viruses in the subfolder "codon_usage_scores_viruses_vs_host" are organised by viral family and genus in separate sub-subfolders. The "Genome_annotations_amoebae" folder contains the generated genome annotations in different formats and the manually curated mitochondrial genome annotations for each amoeba host. The "Phylogenetic_trees_18S_amoebae" contains for the eukaryotic phyla <em>Discosea</em>, <em>Heterolobosea</em>, and <em>Tubulinea, </em>the 18S rRNA nucleotide alignments, distance matrices, and computed phylogenetic trees. The folder "Phylogenomic_trees_amoebae" contains for the eukaryotic clades <em>Amoebozoa</em> and <em>Discoba, </em>the protein alignment matrices and computed phylogenomic trees. The folder "Viral_integration_detection_amoebae" contains the MCP databases used (fasta file, alignment file, HMM profile and DIAMOND BLASTX database) and the MCP sequences detected in this study and the blast results of these. </p>
Supplementary data for Model-driven engineering of Cutaneotrichosporon oleaginosus ATCC 20509 for improved microbial oil production
<p>Supplementary data corresponding to manuscript named Model-driven engineering of <em>Cutaneotrichosporon oleaginosus</em> ATCC 20509 for improved microbial oil production. </p> <p>The Supplementary material document contains supplementary figures and tables. The content of the figures and tables are indicated below. </p> <ul> <li>Figure S1. Plasmid map of pUC57NAT containing pGpd, nourseothricin acyltransferase gene and tGpd.</li> <li>Figure S2. Plasmid maps of overexpression targets containing TEF1α promoter, ATP-citrate lyase gene, TEF1α terminator, TPI1 promoter, Acetyl-CoA carboxylase gene, TPI1, YAT1 promoter, threonine synthase gene, YAT1 terminator and ENO1 promoter, hydroxymethylglutaryl-CoA synthase gene, ENO1 terminator.</li> <li>Table S2. Nucleotide sequences of promoters, genes, and terminators from <em>C. oleaginosus.</em></li> <li>Figure S3. Calibration curve of glycerol for calculating the glycerol concentration of medium.</li> <li>Figure S4. Volcano plots displaying differentially expressed genes and fold change (log2) in expression levels in WT, Δ9 and Δ12 strains at low lipid accumulation vs high lipid accumulation conditions.</li> <li>Figure S5. Flux distribution graphs of selected reactions for overexpression in C. oleaginosus.</li> <li>Figure S6. Colony PCR products were run on 1 % agarose gel. The colony PCR was performed for WT, ACL, ACC and TS transformants.</li> <li>Table S4. qPCR outputs, CT: The threshold cycle.</li> <li>Table S5. Fatty acid profile of C. oleaginosus grown at minimal medium with or without supplement (biotin, thiamine, threonine, serine, and aspartate) at 96h.</li> <li>Table S6. Lipid content, dry cell weight, and lipid weight of WT, ACL, ACC, TS, and HMGS <em>C. oleaginosus</em> at various C/N ratio minimal medium.</li> <li>Table S7. Fatty acid profile of WT, ACL, ACC, HMGS, and TS grown at C/N30, 120, 175, 200, and 300 minimal medium at 96h.</li> <li>Figure S7. Quadratic regression analysis on lipid accumulation, biomass and lipid content of wild-type, ACL, ACC, and TS C. oleaginosus at C/N 30, 120, 175, 200, 300.</li> <li>Table S8. Regression equations, statistics of regression equations for lipid content, biomass, and lipid content of wild-type, ACL, ACC, and TS.</li> <li>Table S9. Calculated optimum C/N ratios and responses (lipid content, biomass, and total lipid) by using built regression models for wild-type, ACL, ACC, and TS.</li> </ul> <p>Authors: </p> <p>Zeynep Efsun Duman-Özdamar<sup>a,b,c</sup>, Mattijs K. Julsing<sup>c</sup>, Janine A.C. Verbokkem<sup>c</sup>, Emil Wolbert<sup>c</sup>, Vitor A.P. Martins dos Santos<sup>a,b,d</sup>, Jeroen Hugenholtz<sup>e,f</sup>, Maria Suarez-Diez<sup>b*</sup></p> <p><sup>a</sup>Bioprocess Engineering, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands</p> <p><sup>b</sup>Laboratory of Systems and Synthetic Biology, Wageningen University & Research, 6708 WE, Wageningen, the Netherlands</p> <p><sup>c</sup>Wageningen Food & Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands</p> <p><sup>d</sup>LifeGlimmer GmbH, Berlin, 12163, Germany</p> <p><sup>e</sup>Faculty of Science Swammerdam Institute for Life Sciences, University of Amsterdam, 1090 GE Amsterdam, The Netherlands</p> <p><sup>f</sup>NoPalm Ingredients BV, 6709 PA Wageningen, The Netherlands</p>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
Data and Supplementary Information : timber ressource dynamics in West African cocoa agroforestry systems
<p>Here we present three datasets (growthDB.csv, heightDB.csv and volumeDB.csv) describing 59 tree species used for wood production in cocoa Agroforestry System (AFS) in Côte d'Ivoire (West Afica). Our dataset includes a total of 4,634 trees: 2530 spontaneous and 2104 (trans)planted. </p> <p>We provide three table (per datasets in .csv format) with the following column per tree:</p> <p>- gensp: species latin names</p> <p>- dbh : tree diameter at breast height (cm)</p> <p>- age: tree age (years)</p> <p>- origin: tree origin (spontaneous or (trans)planted)</p> <p>- WDmean : tree wood density (g.cm³)</p> <p>- clus: study sites</p> <p>- indsp : species index</p> <p>- indclus : sites index</p> <p>- hlog: tree bole height (m)</p> <p>- vol : tree bole volume (m³)</p> <p>We also provide, the STAN code (models.stan) to assess the wood production potential of trees in cocoa AFS: (i) a model describing the diameter growth trajectories of trees as a function of their age, (ii) a model evaluating the relationship between tree diameter and bole height, and (iii) a model assessing the commercial volume of trees as a function of their diameter and bole height.</p> <p>Finally, we present additional tables and figures in Supplementary Information.pdf.</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </p>
Supplementary Data from, "A Mechanistic Model of Annual Sulfate Concentrations in the U.S."
<p>These data are used to perform the analysis contained in, "A Mechanistic Model of Annual Sulfate Concentrations in the United States," by Wikle, Hanks, Henneman, and Zigler. This is purely for archival purposes, to facilitate access and replication of the aforementioned analysis. All data were obtained from the following publicly available sources:</p> <p>1) AMPD Unit Data (U.S. EPA, "Air markets program data," https://ampd.epa.gov/ampd)</p> <p>2) 2010 U.S. Population Density (U.S.G.S., http://dx.doi.org/10.5066/F74J0C6M)</p> <p>3) SO4 Concentrations (Randall Martin Atmospheric Composition Analysis Group's North American Regional Estimates, version V4.NA.02, https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03)</p> <p>4) North American Regional Reanalysis Meteorological Data (NOAA, https://psl.noaa.gov/data/gridded/data.narr.monolevel.html)</p> <p>Code and supplementary material from this analysis are available at: https://github.com/nbwikle/mechanisticSO4-supp_material</p>
Supplementary data for "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise"
<p>This dataset comprises topography and bathymetric data at three coastal locations in Australia (Narrabeen), UK (Perranporth) and used for the publication "Single extreme storm sequence can offset decades of predicted shoreline retreat by sea-level rise". Please refer to readme files for metadata</p>
Supplementary data for "Effect of Human Disturbance on Bird Telomere Length: An Experimental Approach"
<p><strong>Abstract</strong></p> <p>Human recreational activities increase worldwide in space and frequency leading to higher rates of encounter between humans and wild animals. Because wildlife often perceive humans as predators, this increase in human disturbance may have negative consequences for the individuals and also for the viability of populations. Up to now, experiments on the effects of human disturbance on wildlife have mainly focused on individual behavioral and stress-physiological reactions, on breeding success, and on survival. However, the effects on other physiological parameters and trans-generational effects remain poorly understood. We used a low-intensity experimental disturbance in the field to explore the impacts of human disturbance on telomere length in great tit (<em>Parus major</em>) populations and found a clear effect of disturbance on telomere length. Adult males, but not females, in disturbed plots showed shorter telomere lengths when compared to control plot. Moreover, variation in telomere length of adult great tits was reflected in the next generation, as we found a positive correlation between telomere length of the chicks and of their fathers. Given that telomere length has been linked to animal lifespan, our study highlights that activities considered to be of little concern (i.e., low levels of disturbance) can have a long-lasting impact on the physiology and survival of wild animals and their next generation.</p>
Supplementary data for publication Global distribution of mcr gene variants in 214K metagenomic samples
<p># Supplementary data for the manuscript "Global distribution of mcr gene variants in 214,095 metagenomic samples"</p> <p>SD1_mapped_runids.csv : tab-separated file with columns of run_accessions downloaded from ENA and whether the metagenome were positive for at least one of the mcr genes.</p> <p>SD2_mcr_df.csv : compositional table of mcr-positive metagenomes with associated metadata (collection_year, country, and host) for each run_accession, as well as mapping results.</p> <p>SD3_mcr_contigs.fa : FASTA file with contigs carrying mcr genes. The header contains the run_accession ID.</p> <p>SD4_aldex2_results.csv: CSV file containing ALDEx2 results. The columns are as follows:<br> * group: metadata category (year, country or host). If the column contains more than one label, e.g., "Denmark - 2020 - Pigs", significance is tested within Danish pig samples from 2020.<br> * rab.all: median clr value for all samples in the feature<br> * rab.win.conditionA: median clr value for the condition A of samples<br> * rab.win.conditionB: median clr value for the condition B of samples<br> * diff.btw: median difference in clr values between A and B conditions<br> * diff.win: median of the largest difference in clr values within A and B conditions<br> * effect : median effect size: diff.btw / max(diff.win) for all instances<br> * overlap : proportion of effect size that overlaps 0 (i.e. no effect)<br> * we.ep: Expected P value of Welch’s t test<br> * we.eBH: Expected Benjamini-Hochberg corrected P value of Welch’s t test<br> * wi.ep: Expected P value of Wilcoxon rank test<br> * wi.eBH: Expected Benjamini-Hochberg corrected P value of Wilcoxon test<br> * parts: gene name<br> * conditionA: label of condition A that is compared against condition B<br> * conditionB: label of condition B that is compared against condition A<br> * conditions.A.vs.B: label to explain condition A compared against condition B<br> NOTE: see for more explanation of the output of ALDEx2 https://www.bioconductor.org/packages/release/bioc/vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html#5_ALDEx2_outputs</p> <p>SD5: Multi-VCF file containing SNP information on mcr alleles. Can be used to construct consensus sequences.</p> <p>SD6: FASTA file containing all unique consensus sequences reported in the manuscript.</p> <p>SD7: CSV file with an overview of which metagenome contains which unique consensus sequence.</p>
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
<p>This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a></p> <p>The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters:</p> <ul> <li> <p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p> </li> <li> <p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p> </li> <li> <p>EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface.</p> </li> <li> <p>ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970).</p> </li> <li> <p>fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages.</p> </li> <li> <p>port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved.</p> </li> <li> <p>freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message.</p> </li> <li> <p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p> </li> <li> <p>dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate.</p> </li> <li> <p>ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic.</p> </li> <li> <p>gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW.</p> <ul> <li> <p>rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>].</p> </li> <li> <p>snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level.</p> </li> <li> <p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p> </li> <li> <p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p> </li> <li> <p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p> </li> <li> <p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p> </li> <li> <p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> <li> <p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> </ul> </li> <li> <p>bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%).</p> </li> <li> <p>data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements.</p> </li> <li> <p>device_Lat—Latitude of the measurement point gathered from the GPS.</p> </li> <li> <p>device_Lon—Longitude of the measurement point gathered from the GPS.</p> </li> </ul> <p>The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.