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10,478 results for “variation”
Habitat range and phenotypic variation in twelve salt marsh plants on Sapelo Island, Georgia, USA.
We measured traits of twelve salt marsh plant species at sites around Sapelo Island GA in August of 1999. Plant species represent six families - Asteraceae: Aster tenuifolius L., Borrichia frutescens L., Iva frutescens L.; Bataceae: Batis maritima L.; Chenopodiaceae: Salicornia bigelovii Torrey, Salicornia virginica L.; Juncaceae: Juncus roemerianus Scheele; Plumbaginaceae: Limonium carolinianum (Walter) Britton; Poaceae: Distichlis spicata (L.) Greene, Spartina alterniflora Loisel., Spartina patens (Aiton) Muhl., Sporobolus virginicus (L.) Kunth; all nomenclature follows Radford et al. (1968). Soil cores were taken adjacent to each plant to measure soil water content, porewater salinity, and organic content.
Linking Xylem Diameter Variations with Sap Flow Measurements at Harvard Forest 2003-2006
Measurements of variation in the diameter of tree stems provide a rapid response, high resolution tool for detecting changes in water tension inside the xylem. Water movement inside the xylem is caused by changes in the water tension and theoretically, the sap flow rate should be directly proportional to the water tension gradient and, therefore, also linearly linked to the xylem diameter variations. The coefficient of proportionality describes the water conductivity and elasticity of the conducting tissue. Xylem diameter variation measurements could thus provide an alternative approach for estimating sap flow rates, but currently we lack means for calibration. On the other hand, xylem diameter variation measurements could also be used as a tool for studying xylem structure and function. If we knew both the water tension in the xylem and the sap flow rate, xylem conductivity and/or elasticity could be calculated from the slope of their relationship. In this study we measured diurnal xylem diameter variation simultaneously with sap flow rates (Granier-type thermal method) in six deciduous species (Acer rubrum L., Alnus glutinosa Miller, Betula lenta L., Fagus Sylvatica L. Quercus rubra L., and Tilia vulgaris L.) for 7-91 day periods during summers 2003, 2005 and 2006 and analyzed the relationship between these two measurements. We found that in all species xylem diameter variations and sap flow rate were linearly related in daily scale (daily average R 2 = 0.61-0.87) but there was a significant variation in the daily slopes of the linear regressions. The largest variance in the slopes, however, was found between species, which is encouraging for finding a species specific calibration method for measuring sap flow rates using xylem diameter variations. At a daily timescale, xylem diameter variation and sap flow rate were related to each other via a hysteresis loop. The slopes during the morning and afternoon did not differ statistically significantly from each other,
Regional and Historical Variation in Garlic Mustard Distribution in Western Massachusetts 2006-2007
The susceptibility of a site to invasion by nonnative species depends on its current ecological features and its historical land use. Certain environments might be more conducive to an invasive plant’s success, and several recent studies have shown that former agricultural sites are more susceptible to invasion than sites that have been continuously wooded. We studied the invasive herb garlic mustard (Alliaria petiolata), at roadside forested edges. Site selection was stratified by two regions with distinct ecological characteristics (the Connecticut River Valley and the Berkshire Valley in Massachusetts), and two historical land uses (wooded versus cleared in 1830).
Macrophyte and microbial mat biomass co-variation along a hydrologic gradient and response to a removal experiment in temporary wetlands Everglades, FL, USA, February 2003 – November 2006
This data package encompasses hydrologic variables, soil depth, hydrologically-regulated macrophyte community types, macrophyte biomass and community structure, and microbial mat biomass that was collected in two observational surveys and one in-situ experimental manipulation in six temporary wetland regions located in the Everglades, FL, USA. The goal of this project was to examine the co-variation in macrophyte and microbial mat biomass along the hydrologic gradient present across wetland regions and to determine the type and strength of interactions occurring between the two communities, which was tested using a biomass (macrophyte or microbial mat) removal experiment. The census observational survey took place at 140 sites from 2003-04-09 to 2004-05-26, which were randomly distributed across the hydrologic gradient present across the six temporary wetland regions. The transect observational survey occurred along six transects and each was deliberately established along the present hydrologic gradient within each region; a total of 254 sites were sampled from 2003-02-19 to 2005-03-04. The experiment took place at three temporary wetland sites with contrasting hydroperiods (3 – 6 months), and four transects were established per site with 24 pairs of control and treatment plots per transect. The removal treatment occurred one year before data collection, and data collection occurred from 2004-06-20 to 2006-11-25. The package includes six datasets, one R code file, and two shape files associated with the R code. Data collection for all datasets is complete. FCE1274_Census_Survey includes hydrologically-regulated macrophyte community type classifications, macrophyte biomass, microbial mat ash-free dry mass, mean soil depth, water depth, mean annual hydroperiod, and vegetation-inferred hydroperiod; each site was sampled once during the survey period and a subset of sites were sampled each year. FCE1274_Transect_Survey includes macrophyte community type classifications
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping
<h3>TCGA pan-cancer mRNA and DNA data augmented with artificial confounders utilised in "Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping" by Zuqi Li and Sonja Katz (manuscript in preparation).</h3> <p>The following data curation steps were carried out: </p> <ul> <li><strong>Step 1. Download data from TCGA</strong> <ul> <li>R package `TCGAbiolinks`</li> <li>2547 patients (after step 2) with 6 cancer types: <ul> <li>BRCA (731)</li> <li>THCA (408)</li> <li>BLCA (387)</li> <li>LUSC (297)</li> <li>HNSC (412)</li> <li>KIRC (312)</li> </ul> </li> <li>mRNA expression profiles</li> <li>DNAm expression profiles</li> <li>Clinical data: <ul> <li>tumor stage: i, ia, ib, ii, iia, iib, iii, iiia, iiib, iiic, iv, iva, ivb, ivc, x</li> <li>age at diagnosis</li> <li>race: 'white', 'black or african amarican', 'asian', 'american indian or alaska native'</li> <li>gender<br><br></li> </ul> </li> </ul> </li> <li><strong>Step 2. Removal criteria</strong> <ul> <li>Patients with <ul> <li>NA or 'not reported' clinical data</li> <li>race 'american indian or alaska native'</li> <li>tumor stage x</li> </ul> </li> <li>mRNA and DNAm probes with <ul> <li>0 variance across all included patients</li> <li>not shared across all cancer types</li> <li>with missing values<br><br></li> </ul> </li> </ul> </li> <li> <strong>Step 3. Encode clinical vairables and save datasets</strong> <ul> <li>mRNA dataset: 2547 patients x 58,456 mRNAs</li> <li>DNAm dataset: 2547 patients x 232,088 DNAm</li> <li>clinic dataset: 2547 patients x 6 variables<br> 1. patient ID<br> 2. tumor stage: 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4<br> 3. age at diagnosis<br> 4. race: asian(1), black or african amarican(2), white(3)<br> 5. gender: female(0), male(1)<br> 6. cancer type: BRCA(1), THCA(2), BLCA(3), LUSC(4), HNSC(5), KIRC(6)<br> </li> </ul> </li> <li><strong> Step 4. Pre-process the datasets</strong> <ul> <li>mRNA dataset: '<em>TCGA_mRNAs_processed.csv'</em><br> <ul> <li>Take the 2000 mRNAs with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 mRNAs</li> </ul> </li> <li>DNAm dataset: <em>'TCGA_DNAm_processed.csv'</em><br> <ul> <li>Take the 2000 DNAm with highest variance</li> <li>Rescale every feature to [0,1]</li> <li>--> 2547 patients x 2000 DNAm</li> </ul> </li> <li>clinic dataset:<em> 'TCGA_clinic.csv'<br><br></em></li> </ul> </li> <li><strong>Step 5. Simulate confounders (instructions can be found in Methods section of manuscript)</strong> <ul> <li>Linear confounder: <ul> <li><em>'TCGA_confounder_linear.csv' -</em> linear confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_linear.csv' </em>- linearly confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_linear.csv' </em> - linearly confounded mRNA data<em><br></em></li> </ul> </li> <li>Squared confounder <ul> <li><em>'TCGA_confounder.csv' -</em> squared confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded.csv' </em>- squared confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded.csv' </em> - squared confounded mRNA data</li> </ul> </li> <li>Categorical confounder <ul> <li><em>'TCGA_confounder_categ2.csv' -</em> categorical confounding classes<em><br></em></li> <li><em>'TCGA_DNAm_confounded_categ2.csv' </em>- categorically confounded DNAm data<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_categ2.csv' </em> - categorically confounded mRNA data</li> </ul> </li> <li>Multiple confounders - combined effect (linear + squared + categorical)<br> <ul> <li><em>'TCGA_confounder_multi.csv' -</em> confounding classes for combined effect<em><br></em></li> <li><em>'TCGA_DNAm_confounded_multi.csv' </em>- DNAm data with combined effect<em><br></em></li> <li><em>'TCGA_mRNA2_confounded_multi.csv' </em> - mRNA data with combined effect</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Raw data mzXML and MATLAB code for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea
<p>MATLAB code and raw data mzXML for Variation in chemical composition of dissolved organic matter during the winter to spring transition in the northern Barents Sea.</p> <p>Seawater samples were collected during three distinct periods: early winter (December 2019), late winter (March 2021), and spring (May 2021). The sampling transect extended from the northern Barents Sea into the Nansen Basin (76°N – 83°N) as part of <em>The Nansen Legacy</em> project (Research Council of Norway, RCN #276730). The molecular composition of dissolved organic matter (DOM) was analyzed using an Orbitrap mass spectrometer.</p>
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission by Roman et al.
<p>Images of Neptune as used in <a href="https://arxiv.org/abs/2112.00033"><strong>Imaging Data from Sub-Seasonal Variation in Neptune's Mid-Infrared Emission </strong></a>by Michael T. Roman, Leigh N. Fletcher, Glenn S. Orton, Thomas K. Greathouse, Julianne I. Moses, Naomi Rowe-Gurney, Patrick G. J. Irwin, Arrate Antunano, James Sinclair, Yasumasa Kasaba, Takuya Fujiyoshi, Imke de Pater, Heidi B. Hammel.</p> <p>Data are from various observatories/telescope instruments, including:</p> <ul> <li>The European Southern Observatory's Very Large Telescope, VISIR</li> <li>Keck Observatory, LWS</li> <li>Subaru Observatory, COMICS</li> <li>Gemini North, Michelle and TEXES</li> <li>Gemini South, T-ReCS</li> </ul> <p>Data were acquired from observatory archives and flux calibrated, when possible, by comparison to standard stars, with spectral radiances expressed in units of W/m<sup>2</sup>/sr/micron. North is up in the images. The first extension (ext=0) is the image in native spatial resolution. The second extension (ext=1) features the disk normalized in size to that of the finest data (i.e., to a disk with an equatorial width of 51.8 pixels, as imaged by VLT-VISIR on August 13, 2018).</p> <p>Image file names and times correspond to approximate mid-time of combined image sequences, and will differ from original file headers.</p> <p>Questions concerning these data should be directed towards Michael Roman, m.t.roman@le.ac.uk or michael.thomas.roman@gmail.com</p> <p> </p> <p> </p>
Isotopic Variation of Soil Macrofossils from Shark River Slough, Everglades National Park (FCE) in December 2004
These data represent stable isotopic signatures of selected macrofossils from soil cores from Shark Slough sites, including FCE LTER site SRS3. Soils from 1-cm depth increments were analyzed for macrofossil content (mainly seeds) and seeds were processed for d13C and d15N isotopic signatures. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Variation in Landsat 8-estimated land surface temperature with elevation from Spartina alterniflora marsh cross sections in the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) site and Virginia Coast Reserve (VCR) LTER sites for winter and summer observations spanning 2013-2018
We estimated land surface temperature from top of atmosphere brightness temperature provided by Landsat 8's band 10 (a thermal band). We collected these measurements first for Spartina alterniflora dominated marsh near the Georgia Coastal Ecosystems Long Term Ecological Research (GCE-LTER) eddy covariance flux tower. Measurements were collected from pixels along three east-west cross sections that spanned a marsh edge to interior gradient. We extracted Landsat 8 data for all available cloud-free low tide dates during August, September, January and February during the years 2013 to 2018 and associated these with marsh elevation information from a 1 m^2 Digital Elevation Model (DEM), created by Haldik et al 2013, also available from the GCE data catalog (http://dx.doi.org/10.6073/pasta/4c5187ef603f70cd0a77ece24ef0fed9). We rescaled the DEM to the coarser spatial resolution of Landsat 8 (30 x 30 m) where the rescaled elevation was the mean of the constituent DEM values. Ultimately, we used generalized additive models to relate land surface temperature to elevation, while accounting for variation from spatial proximity, transect and sample date. These models revealed that land surface temperature was negatively related to marsh elevation on the marsh platform. We then confirmed the generality of this pattern by rederiving these same relationships for three cross sections of Spartina alterniflora marsh at Virginia Coast Reserve (VCR) LTER for winter sampling dates only (data also included here). DEM data for VCR LTER are available at https://www.vcrlter.virginia.edu/gisdata/LIDAR/USGS2015/. We used custom R functions that can convert Landsat 8 top of atmosphere brightness temperature or top of atmosphere radiance from band 10 data to land surface temperature, which are available at https://github.com/jloconnell/convert_top_of_atmosphere_thermal_to_land_surface_temperature. Currently, a provisional land surface temperature product is available on earthexplorer.usgs.gov, w
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
Detecting local variations across metazoan communities in backreef depressions of Reunion Island (Mascarene Archipelago) through environmental DNA survey
<p>The back-reef depressions, or lagoons, of Reunion Island (western Indian Ocean) host a high abundance of organisms living amongst the coral reefs and are critical sites for artisanal fishing, tourism, and shoreline stability for the island. Over time, increasing degradation of Reunionese reefs has been observed due to overexploitation, beach erosion and eutrophication. Efforts to mitigate the impact of these pressures on aquatic organisms include biodiversity surveys primarily performed through visual censuses that can be logistically complex and may unintentionally overlook organisms. Surveys integrating environmental DNA (eDNA) collections have provided rapid biodiversity assessments, while helping to circumvent some limitations of visual surveys. The present study describes the results of an exploratory eDNA survey, which aims to characterize metazoan communities of four Reunionese lagoons located along the west coast of the island. As eDNA surveys first require deliberate study design and optimization for each new context, we sought to establish a modernized workflow implementing specialized equipment to collect and preserve samples to facilitate future studies in these lagoons. During the austral summer of 2023, samples were pumped directly from surface and bottom depths at each site through self-preserving filters which were then processed for DNA metabarcoding using regions of the 12S ribosomal RNA (12S), small ribosomal subunit 18S (18S) and Cytochrome Oxidase I (COI) genes. The survey detected high species richness that varied by site, and in a single collection period, recovered the presence of 60 teleost families and numerous invertebrate taxa, including members of the coral faunal community that are less studied in Reunion. Distinct biological communities were observed at each site, and within a single lagoon, suggesting that these differences are due to site-specific factors (e.g., environmental variables, geographic distance, etc.). Although continued protocol optimization is needed, the present findings demonstrate the successful application of an eDNA-based survey for biodiversity assessment within Reunionese lagoons.</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1977 (Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density.)
Title: Microbial and metabolic variations mediate the influence of childhood and adolescent EDC and trace element exposure on breast density. <br>Species: Homo sapiens <br>Number of samples: 1116 <br>Number of named analytes: 41 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=46 <br>
Morphometric data from: Incongruent molecular and morphological variation in the crab spider Synema globosum (Araneae: Thomisidae) in Europe
<p>Here we provide the complete set of files used by <a href="https://doi.org/10.3897/zookeys.1078.64116">Urfer et al. (2021</a>, see References section below for the complete citation of the publication) for the morphometric and the molecular analysis. In particular, we provide the following documents:</p> <p><br> PART 1: MORPHOMETRIC ANALYSIS</p> <p>- 1_Synema_data_multiple_imputation_mice.R: R-script used for replacing NAs.</p> <p>- 1_Synema_data_NA_imputed.csv: Dataset with raw values (in millimeters) of all 28 specimens used for the morphometric analysis. Each specimen was measured 4 times. NAs replaced using the R-script "Synema_multiple_imputation_mice.R" above. This is the datafile used for all morphometric analyses.</p> <p>- 1_Synema_data_with_NA.csv: Dataset with raw values (in millimeters) of all 28 specimens. Each specimen was measured 4 times. NAs not replaced.<br> <br> - 1_Synema_Reliability.R: R-script for calculating reliability.<br> <br> - 1_Synema_Reliability_supplementary_figure.pdf: Results of reliability analysis presented in a bar plot.</p> <p>- 1_Synema_Reliability_supplementary_table.txt: Results of reliability analysis presented in a table.<br> <br> - 1_Synema_Shape_PCA_and_PCA_Ratio_Spectrum.R: R-script for calculating the shape PCA and the PCA Ratio Spectrum of the first shape PC. You may get the necessary MRA source script from http://doi.org/10.5281/zenodo.4250142<br> <br> - Synema_globosum_AR9379_PV.jpg, Synema_globosum_AR9379_PV.jpg, Synema_globosum_AR9379_PV.jpg, etc.: Photographs taken with a LEICA M205 C stere-omicroscope.</p> <p> 1. Numbers after AR_ refer to the inventory number of the specimens in the Natural History Musuem Bern (NMBE). The specimen number was also used in the data file.<br> 2. The photo named "Synema_globosum_AR9163_with_measurements" shows the position of the measurements. Otherwise, the measurements are not indicated in the raw photos.</p> <p><br> Example image Character name Definition<br> Synema_globosum_AR9163_with_measurements cym.l Cymbium lenght Distance of the anterior margin to the tip of the cymbium<br> Synema_globosum_AR9163_with_measurements cym.b Cymbium breadth widest breadth of the cymbium<br> Synema_globosum_AR9163_with_measurements bul.b Bulb breadth widest breadth of the genital bulbus<br> Synema_globosum_AR9163_with_measurements tib.b Tibia breadth breadth of the tibia base at the patella joint</p>
Supplementary Materials for 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction'
<p><strong>Supplementary materials for the article 'Measuring and assessing indeterminacy and variation in the morphology-syntax distinction' in <em>Linguistic Typology </em>(Vol. and No. TBD).</strong></p> <p>Abstract:</p> <p>We provide a discussion of some of the challenges in using statistical methods to investigate the morphology-syntax distinction cross-linguistically. The paper is structured around three problems related to the morphology-syntax distinction; (i) the boundary strength problem; (ii) the composition problem; (iii) the architectural problem.<br> The boundary strength problem refers to the possibility that languages vary in terms of how distinct morphology and syntax are or the degree to which morphology is autonomous. The composition problem refers to the possibility that languages vary in terms of how they distinguish morphology and syntax: what types of properties distinguish the two systems. The architecture problem refers to the possibility that languages vary in terms of whether a global distinction between morphology and syntax is motivated at all and the possibility that languages might partition phenomena in different ways.<br> This paper is concerned with providing an overarching review of the methodological problems involved in addressing these three issues. We illustrate the problems using three statistical methods: correlation matrices, random forests with different choices for the dependent variable, and hierarchical clustering with validation techniques.</p> <p> </p> <p>Overview of materials:</p> <ul> <li>SM1: csv with the data</li> <li>SM2: code and pdf for generating the correlation matrices</li> <li>SM3: code and pdf for the random forest analyses</li> <li>SM4: code and pdf for the clustering and cluster validation analyses</li> </ul>
Dataset supporting the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces. J. Phys. Chem Lett. 12, 2983 (2021)"
<p>Dataset corresponding to theoretical calculations in the supporting information of the paper "Superconducting Scanning Tunneling Microscope Tip to Reveal Sub-millielectronvolt Magnetic Energy Variations on Surfaces" J. Phys. Chem Lett. 12, 2983 (2021), <a href="https://doi.org/10.1021/acs.jpclett.1c00328">https://doi.org/10.1021/acs.jpclett.1c00328</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the supporting information. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (http://www.wsxm.eu/) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr files: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Atlas of the Solar Intensity Spectrum and its Center-to-Limb Variation
<p>The atlas of the Third Solar Spectrum (SS3) represents the ratio between the intensity spectrum at different distances from the solar limb and the intensity spectrum at disk center (µ = 1.0), both in units of the intensity of the local continuum level. The observed positions of the measurements cover 9 different µ values along the solar axis ranging from 0.1 to 0.9 in step of 0.1, where µ=cosθ is the cosine of the heliocentric angle θ. The current version of the atlas covers the range 4384- 6610 Å.</p> <p>In the PDF file, the first plot represents the spectrum at the center of the solar disk, recorded at IRSOL. The next 9 plots represent the 3rd solar spectrum for µ=0.1, µ=0.2, µ=0.3, …, µ=0.9</p> <p>Columns of the CSV file:</p> <table> <tbody> <tr> <td><strong>WL:</strong></td> <td>Wavelength, 4384-6610 Å</td> </tr> <tr> <td><strong>IC:</strong></td> <td>I/I<sub>c</sub> at disc center</td> </tr> <tr> <td><strong>RMU01:</strong></td> <td>limb / disk-center ratio at µ=0.1</td> </tr> <tr> <td><strong>RMU02:</strong></td> <td>limb / disk-center ratio at µ=0.2</td> </tr> <tr> <td><strong>…</strong></td> <td>…</td> </tr> <tr> <td><strong>RMU09:</strong></td> <td>limb / disk-center ratio at µ=0.9</td> </tr> </tbody> </table>
Genome-wide association study suggests that variation at the RCOR1 locus is associated with tinnitus in UK Biobank
<p>The dataset contains results of a genome-wide association studies for age-related hearing impairment (ARHI)-related traits as described in the following publication:<br> Wells, H.R.R., Abidin, F.N.Z., Freidin, M.B. et al. Genome-wide association study suggests that variation at the RCOR1 locus is associated with tinnitus in UK Biobank. Sci Rep 11, 6470 (2021). https://doi.org/10.1038/s41598-021-85871-6</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.