Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,960
datasets available to search
ShareScore release 0.9.0
Dataset results
2,960 results for “elements”
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Soil respiration at Hubbard Brook Experimental Forest, Bartlett Experimental Forest and Jeffers Brook, central NH USA, 2008 - present
Abstract Soil respiration in 15 stands across 3 sites within the White Mountain National Forest was measured between 2008 and 2020. Stands included in the dataset are part of the Multiple Element in Northern Hardwood Ecosystems (MELNHE) study, a full-factorial NxP fertilization experiment. Pre- and post-treatment data are included, with treatment beginning in 2011. Soil temperature, soil moisture, and relative air humidity at the time of measurement were also recorded next to or above the soil respiration collar at the time of the soil respiration measurement. Having been cut between 1883 and 1990, stands are representative of different successional stages.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Foliar Chemistry 2008-2022 in Bartlett, Hubbard Brook, and Jeffers Brook
We are conducting nutrient manipulations in three study sites in the White Mountain National Forest in New Hampshire: Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook. We monitored foliar chemistry in 13 of our stands (including HBCa and excluding C3) pre-treatment (2008-2010) and post-treatment (2014-2016 and 2021-22). In 2021-22, we also measured specific leaf area, leaf dry matter content, carbon isotope composition, and stomatal density. We found that foliar N concentrations were higher with N addition and foliar P concentrations were higher with P addition. More interestingly, P addition reduced foliar N concentrations and N addition reduced foliar P concentrations. Some interactive effects were observed (i.e. NxP, Species x N, Species x P, Species x N x P). This dataset contains pre- and post- treatment foliar chemistry and trait data, and data from the analysis of quality control standard samples. Changes to pre-treatment data from version 1 include switching white birch trees #8272 and #8252 in stand JBM plots 2 and 3 (8272 is now in the nitrogen plot and 8252 is now in the control plot), correcting the species of tree #1628 in stand HBCa plot 1 (changed from red maple to sugar maple) and tree #8457 in stand HBO plot 3 (changed from sugar maple to red maple), and updating nutrient concentrations for C8 plot 3 sugar maple trees #28 and #30 to include averages of subsamples re-run in 2022. Tree tags were also updated to the tag ID present during the 2023 tree inventory. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained
Atmospheric Gaseous Elemental Mercury Fluxes at Harvard Forest EMS Tower 2019-2020
In terrestrial ecosystems, dry deposition of atmospheric gaseous elemental mercury (GEM) is considered the dominant source of mercury accounting for 54% to 94% of mercury loads observed in soils, yet direct quantification of GEM deposition across forests is largely missing. The goal of this project is to quantify atmosphere-surface exchange of GEM at Harvard Forest for one full year, providing the first such record in a non-polluted forest. GEM exchange is measured using micrometeorological techniques using a large measurement tower, the only available method for direct, non-intrusive and time-extended measurements of net GEM exchange at the ecosystem level encompassing all underlying sinks and sources. A second objective was to partition GEM fluxes into canopy and soil contributions via deployment of two corresponding flux systems: one system was deployed above the forest canopy to measure ecosystem-level GEM exchange; a second system was deployed below the canopy to quantify soil contributions. This dataset contains an 18-month record of gaseous elemental mercury concentrations and fluxes measured at the EMS tower at Harvard Forest from May 2019 to August 2020.
ColoPola: A dataset of colorectal cancer polarimetric images (Mueller matrix elements) for colorectal cancer detection
<p><strong>ColoPola</strong> dataset is <strong>Colo</strong>rectal cancer <strong>Pola</strong>rimetric images dataset</p> <p>The dataset consists of 572 slices (specimens) with 20,592 images, 284 slices of which were designated as cancer samples and 288 as normal samples.</p> <p>Each sample has 36 polarimetric images (i.e., HH, HV, HP, HM, HR, HL, VH, VV, VP, VM, VR, VL, PH, PV, PP, PM, PR, PL, MH, MV, MP, MM, MR, ML, RH, RV, RP, RM, RR, RL, LH, LV, LP, LM, LR, and LL).</p> <p>Each folder in the <strong>ColoPola</strong> dataset consists of 36 polarimetric images. Each image is 1280x1024 pixels in size and was created in the TIF file format (HH.tif, HV.tif, ..., LL.tif). </p>
Trajectory Design for Proximity Operations: The Relative Orbital Elements' Perspective
<p>The data sets provided here can be used to recreate the plots of the paper “Trajectory Design for Proximity Operations: The Relative Orbital Elements’ Perspective” available at this <a href="https://arc.aiaa.org/doi/full/10.2514/1.G006175">link</a>.</p> <p>That paper presents how to rigorously transform back-and-forth the equations of the relative motion in the close-range regime between Hill-Clohessy-Wiltshire and Relative Orbital Elements formulations. As straightforward application, it is presented a methodology to generate piecewise constant acceleration profiles from an impulsive guidance solution, setting up a control grid that minimizes the difference between impulsive and equivalent delta-v burns corresponding to the acceleration profile.</p> <p>Applications are implementation of autonomous guidance and control policies for close-range satellite proximity operations.</p>
Limno-STOICH a comprehensive database linking the elemental content of organisms with inland, aquatic habitats (2025-12-11)
The Limnology Stoichiometric Traits of Organisms In their Chemical Habitats (Limno-STOICH) contains >51,000 observations of organismal elemental content fro >3,100 rivers, lakes, wetlands, and other aquatic ecosystem sites on seven continents. The data are derived from 190+ sources including author contributed collections, novel NEON-related data, and published datasets. The database also includes extensive spatial and temporal metadata to link elemental stoichiometry with ecosystem type, trophic status, etc., and information on organismal data (body size, taxonomic classifications, stable isotope composition) and water physicochemical parameters, as available. Users are encouraged to read the associated manuscript (Corman et al.) for further information.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Salt Exchangeable Cation Extractions from Hubbard Brook and Bartlett sites
Soil element concentrations (Na, Mg, K, Ca, Al, Mn, Fe, Si, Sr, and Ba) were measured in salt exchangeable extracts of soil samples taken in July 2017 in the MELNHE study, specifically in Bartlett stands C1-C9 and Hubbard Brook stands HBM and HBO. Additional detail on the MELNHE project, including a datatable of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. Additional analysis data on these samples can be found in the dataset "Soil properties in the MELNHE study at Hubbard Brook Experimental Forest, Bartlett Experimental Forest and Jeffers Brook, central NH USA, 2009 - present" (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=165). This work is a contribution of the Hubbard Brook Ecosystem Study. Hubbard Brook is part of the LTER network, which is supported by the US National Science Foundation. The Hubbard Brook Experimental Forest is operated and maintained by the US Department of Agriculture, Forest Service, Northern Research Station.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Root cores and mycorrhizal colonization
Root cores were obtained in 2010 (pre-treatment) from two soil depths, 0-10 cm and 30-50 cm, in two MELNHE stands, C5 and C7, at Bartlett Experimental Forest. Arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) colonization and root length were quantified in each core to determine if AM or EM was more prevalent in shallow or deep soils. Detailed description and analyses of these data can be found in: Nash, J.M., Diggs, F.M. & Yanai, R.D. Length and colonization rates of roots associated with arbuscular or ectomycorrhizal fungi decline differentially with depth in two northern hardwood forests. Mycorrhiza 32, 213–219 (2022). https://doi.org/10.1007/s00572-022-01071-8 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Fresh Litter Chemistry
Freshly senesced leaf litter was collected during autumn in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaf litter was collected in October of 2009, 2010, 2014, 2015, 2016, 2021, and 2022 at peak litterfall (i.e., mid-October) during a rain-free period. These leaf-litter samples were analyzed for nutrient concentrations for use in resorption analyses. Besides adding 2021 and 2022 to the previous version of this data package, this version includes updated values for some samples from 2009 and 2010. Some were re-run to check unusual values, and 8 samples from 2010 for which fresh litter was not collected were estimated by analyzing litter samples collected in litter traps in the same plots in that year. These additions and corrections are indicated in the comments section of the data. These leaf litter samples correspond with green foliage samples collected in late July and early August of the same years: the green foliage EDI package can be found at the following citation: Zukswert, J.M., S.D. Hong, K.E. Gonzales, C.R. See, and R.D. Yanai. 2025. Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Foliar Chemistry 2008-2022 in Bartlett, Hubbard Brook, and Jeffers Brook ver 4. Environmental Data Initiative. https://doi.org/10.6073/pasta/ef3696a753150d0a420fd9009f73b1e9 (Accessed 2025-01-13). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) - stomatal density and length 2021-2022
Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the stomatal density and length derived from the raw images in a separate EDI data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=321 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Finite element method (FEM) models for translational research in non-invasive brain stimulation
<p>Finite element method (FEM) models for non-invasive brain stimulation modeling using SimNIBS or other compatible software.<br> The mouse and monkey models are described in detail in Alekseichuk et al., Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human, NeuroImage 2019.<br> The Petri dish model follows a typical experimental setup for in-vitro TMS, similar to what is described in Lenz et al. Repetitive magnetic stimulation induces plasticity of inhibitory synapses, Nature Communications 2016.<br> <br> The following files are included:<br> 1. Brain tissue slice in a Petri dish.<br> 2. Normal adult male nude mouse "Digimouse" (brain volume of 0.38 cm3).<br> 3. Normal adult male capuchin monkey "S" (brain volume of 68.31 cm3).<br> <br> The models include the following tissues (coded with numbers):<br> 1. White matter volume<br> 2. Grey matter volume<br> 3. CSF volume<br> 4. Skull volume<br> 5. Soft tissues volume<br> 8. Eyeballs volume<br> 1001. White matter outer surfaces<br> 1002. Grey matter outer surfaces<br> 1003. CSF outer surfaces<br> 1004. Skull outer surfaces<br> 1005. Soft tissues outer surfaces<br> 1008. Eyeballs outer surfaces<br> <br> With any questions, please, contact the corresponding authors of the relevant papers or <a href="mailto:aopitz@umn.edu">aopitz@umn.edu</a> (Alexander Opitz).</p>
Evolution of software code at the level of fine-grained elements: data files
<p>The data files available here (68GB uncompressed) have been used for studying the evolution of code at the level of fine-grained elements. The data are associated with the processing of the 89 open source software repositories hosted on GitHub. Details regarding each individual GitHub project are stored in the repos folder under directories matching the owner and project name used on GitHub. For example, the files under repos/KDE/kdevelop correspond to the project hosted on https://github.com/KDE/kdevelop. Data associated with the statistical analysis of the processed repositories are stored in the statistical-analysis folder. The file project_details.txt contains the data used for selecting the processed projects.</p>
Learning Elements in Learning Management Systems (LMSs)
<p>Results of a survey in the higher education area. Participants are professors, lecturers, and tutors.</p> <p> </p> <p>The final definitions for the elements are:</p> <ul> <li>Brief Overview (BO): Short summary or recap without details of the actual learning material</li> <li>Quiz (QU): Quiz questions related to the content taught</li> <li>Learning Goal (LG): Description of the competences, skills or abilities that the learners should acquire in relation to a specific learning content</li> <li>Manuscript (MS): Complete or brief elaboration of a speech, a lecture, a course, or similar</li> <li>Exercise (EX): Opportunity to apply and deepen the learned. Varied tasks are possible beside the classic exercise sheet</li> <li>Summary (SU): Elementalization (reduction to the essentials) of the actual content with details</li> <li>Auditory additional material (AAM): Material with the aim of applying and deepening the learned with audio files</li> <li>Textual additional material (TAM): Material with the aim of applying and deepening the learned with textual further information (also named additional literature)</li> <li>Visual additional material (VAM): Material with the aim of applying and deepening the learned with videos or similar</li> <li>Collaboration Tool (CT): Cooperative and interactive communication medium with the aim of knowledge sharing between learners and learners and/or lecturers, and is used for collaborative work</li> </ul> <p>The corresponding scientific paper can be found via ORCID as of December 2023.</p> <p> </p> <p>The presented work is supported by the ‘German Federal Ministry of Research, Technology and Space’ (BMFTR) through the granting of the funding project HASKI (FKZ: 16DHBKI035).</p>
Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements
<p>This a dataset set to paper entitled: "Case study of self-compacting, fiber reinforced, lightweight concrete, intended for production of precast elements".</p> <p>Dataset is one excel file divided in various sheets containing:</p> <ol> <li>Properties of used aggregates</li> <li>Initial properties of concrete</li> <li>Composition of concrete</li> <li>Concrete with fibres</li> <li>Final concrete</li> </ol>
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>
Dissecting the FAIR Guiding Principles - Key Categories, Core Concepts, Focus Elements, and Harmonized Indicators
<p>A comprehensive workbook created to facilitate and document the process of decomposing the FAIR Guiding Principles and mapping them to key categories, requirements, core concepts, focus elements, and harmonized indicators. It also contains a complete list of the indicators.</p>
Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to <em>AGU Geophysical Research Letters</em>.</p> <p> </p> <p><strong>file01</strong> contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022.</p> <p><strong>file02</strong> contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file03</strong> contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file04</strong> contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file05</strong> contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file06</strong> contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file07</strong> contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file08</strong> contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO.</p> <p><strong>file09</strong> contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file10</strong> contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file11</strong> contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file12</strong> contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file13</strong> contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file14</strong> contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file15</strong> contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file16</strong> contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file17</strong> contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022.</p> <p><strong>file18</strong> contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file19</strong> contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file20</strong> contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022.</p> <p><strong>file21</strong> contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.</p> <p> </p> <p><strong>file22</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file23</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file24</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file25</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file26</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file27</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file28</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file29</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file30</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file31</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file32</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file33</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file34</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file35</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file36</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file37</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file38</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file39</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p> </p> <p><strong>file40</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file41</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file42</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file43</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file44</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file45</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file46</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file47</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file48</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file49</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file50</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file51</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file52</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file53</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file54</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file55</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file56</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file57</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p>
Soil and meteorological data, and finite element simulation framework for heat transfer through shrubs in winter near Lautaret pass, French Alps
<p>The data allow the calculation using finite element modeling of heat transfer through shrub branches and snow between the atmosphere and the soil. The shrubs are green alders (Alnus viridis). The site where they are found is called Alnus-Nivus (45.034750°N, 6.413630°E, 2034 m asl) near Col du Lautaret, French Alps. The soil data consist in temperature and volumetric liquid water content at 5 and 15 cm depths. One spot is near the alder collar (ALNUS), the other spot is 6 m away, under grass (GRASS).</p> <p>The meteorological data were obtained from the FR-Clt station, 750 m away (45.041278°N, 6.410611°E, 2046 m asl). See (Gupta et al., 2023) for details. Only the data relevant for heat transfer simulations are given.</p> <p>The simulation framework gives the alder mesh used in the heat transfer simulations. Typical simulations use a wood thermal conductivity of 1 W m<sup>-1</sup> K<sup>-1</sup> and a snow thermal conductivity of 0.1 W m<sup>-1</sup> K<sup>-1</sup>. Based on observations, the snow height at Alnus-Nivus is likely to be at least twice the value at FR-Clt. Forcing uses the snow surface temperature, derived from upwelling longwave radiation using an emissivity of 1. The data allow testing thermal bridging through shrub branches. These data are used in a publication in preparation: Domine, Fourteau, Choler, Exploration of Thermal Bridging Through Shrub Branches in Alpine Snow.</p> <p>Reference</p> <p>Gupta, A., Reverdy, A., Cohard, J. M., Hector, B., Descloitres, M., Vandervaere, J. P., Coulaud, C., Biron, R., Liger, L., Maxwell, R., Valay, J. G., and Voisin, D.: Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment, Hydrol. Earth Syst. Sci., 27, 191-212, 2023.</p>
The Elements of the ENVRI-Hub
<p>The illustration visualises the elements of the ENVRI-Hub, the open-access platform of the environmental sciences community in Europe.</p> <p>The ENVRI-Hub and its elements are accessible through <a href="https://envri-hub.envri.eu/">https://envri-hub.envri.eu/</a>.</p>
A pangenome-guided manually curated library of transposable elements for Zymoseptoria tritici
<p>A manually-curated TE consensus library generated using a panel of 19 reference genomes for <em>Zymoseptoria tritici</em><sup>1-3</sup> along with reference genome assemblies for the sister species <em>Z. ardabiliae</em>, <em>Z. brevis</em>, <em>Z. pseudotritici</em>, and <em>Z. passerinii<sup>4</sup></em>. </p> <p> </p> <p><strong>Methods</strong></p> <p>Putative TE consensus sequences were first obtained by annotating all 23 genome assemblies<sup>1–4</sup> with Earl Grey with default settings (v3.0; <a href="https://github.com/TobyBaril/EarlGrey">https://github.com/TobyBaril/EarlGrey</a>)<sup>5,6</sup>. Consensus sequences generated from each reference genome were clustered using CD-Hit-Est (v4.8.1)<sup>7,8</sup> to group sequences with 90% similarity across 80% of the longer sequence length (<em>-n 8 -d 0 -aL 0.8 -c 0.90 -G 0 -g 1 -b 500 -r 1</em>) to reduce redundancy whilst preventing the collapsing of chimeric sequences. Consensus sequences <100bp were removed, as these are unlikely to represent true TE sequences. Each consensus sequence was then subject to manual curation as described by Goubert et al. (2022)<sup>9</sup>. Briefly, genomic copies of each TE were obtained using a “BLAST, Extract, Extend” process to recover genomic copies from each of the 23 reference genome assemblies with 1,000 flanking bases at either end<sup>9,10</sup>. For families with >100 BLASTN hits, the 25 longest hits were selected, along with 75 random hits. Multiple alignments were generated for each putative TE family using MAFFT (v7.505) with the --auto flag<sup>11</sup>. Columns composed of >=80% gaps were removed with T-COFFEE (v13.45.0.4846264)<sup>12</sup>. Subsequently, all sequence alignments were manually curated to define TE boundaries and remove regions of low conservation and rare insertions. Following manual curation, new majority-rule consensus sequences were generated with EMBOSS (v6.6.0.0) cons<sup>13</sup>. TE-Aid (<a href="https://github.com/clemgoub/TE-Aid/">https://github.com/clemgoub/TE-Aid/</a>) was used to aid visual inspection and to identify diagnostic features for classification of extended consensus sequences. Following this, TIRs were recorded if present, and nhmmscan (HMMER v3.3.2)<sup>14</sup> was used to identify homology to known curated elements in Dfam (v3.7). Combining this information, each TE consensus sequence was manually classified using available information following the naming convention ‘>ZymTri_2023_family_[n]#[Classification]/[Family]’. Consensus sequences classified with low confidence have a ‘?’ added to the name, as well as the string ‘_LowConf’. To reduce redundancy in the final TE library, sequences were clustered to the family-level using the 80-80-80 rule implemented in CD-hit-est<sup>9,15 </sup>(<em>-d 0 -aS 0.8 -c 0.8 -G 0 -g 1 -b 500 -r 1</em>). The representative sequence for each cluster was manually selected to select the sequence with the highest classification confidence, also defined as the ‘most intact consensus’. Chimeric sequences erroneously clustered were manually separated to retain sequences for the chimeric TE and the individual elements that generated the chimer.</p> <p> </p> <p><strong>References</strong></p> <p>1. Badet, T., Oggenfuss, U., Abraham, L., McDonald, B. A. & Croll, D. A 19-isolate reference-quality global pangenome for the fungal wheat pathogen Zymoseptoria tritici. <em>BMC Biol.</em> <strong>18</strong>, 12 (2020).</p> <p>2. Goodwin, S. B. <em>et al.</em> Finished genome of the fungal wheat pathogen Mycosphaerella graminicola reveals dispensome structure, chromosome plasticity, and stealth pathogenesis. <em>PLoS Genet.</em> <strong>7</strong>, e1002070 (2011).</p> <p>3. Plissonneau, C., Hartmann, F. E. & Croll, D. Pangenome analyses of the wheat pathogen Zymoseptoria tritici reveal the structural basis of a highly plastic eukaryotic genome. <em>BMC Biol.</em> <strong>16</strong>, 5 (2018).</p> <p>4. Feurtey, A. <em>et al.</em> Genome compartmentalization predates species divergence in the plant pathogen genus Zymoseptoria. <em>BMC Genomics</em> <strong>21</strong>, 588 (2020).</p> <p>5. Baril, T., Imrie, R. M. & Hayward, A. Earl Grey: a fully automated user-friendly transposable element annotation and analysis pipeline. (2022) doi:10.21203/rs.3.rs-1812599/v1.</p> <p>6. Baril, T., Galbraith, J. & Hayward, A. <em>Earl Grey</em>. (Zenodo, 2023). doi:10.5281/ZENODO.8116025.</p> <p>7. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. <em>Bioinformatics</em> <strong>22</strong>, 1658–1659 (2006).</p> <p>8. Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. <em>Bioinformatics</em> <strong>28</strong>, 3150–3152 (2012).</p> <p>9. Goubert, C. <em>et al.</em> A beginner’s guide to manual curation of transposable elements. <em>Mob. DNA</em> <strong>13</strong>, 7 (2022).</p> <p>10. Camacho, C. <em>et al.</em> BLAST+: Architecture and applications. <em>BMC Bioinformatics</em> <strong>10</strong>, 1–9 (2009).</p> <p>11. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. <em>Mol. Biol. Evol.</em> <strong>30</strong>, 772–780 (2013).</p> <p>12. Notredame, C., Higgins, D. G. & Heringa, J. T-coffee: a novel method for fast and accurate multiple sequence alignment. <em>J. Mol. Biol.</em> <strong>302</strong>, 205–217 (2000).</p> <p>13. Rice, P., Longden, L. & Bleasby, A. EMBOSS: The European Molecular Biology Open Software Suite. <em>Trends Genet.</em> <strong>16</strong>, 276–277 (2000).</p> <p>14. Wheeler, T. J. & Eddy, S. R. nhmmer: DNA homology search with profile HMMs. <em>Bioinformatics</em> <strong>29</strong>, 2487–2489 (2013).</p> <p>15. Wicker, T. <em>et al.</em> A unified classification system for eukaryotic transposable elements. <em>Nat. Rev. Genet.</em> <strong>8</strong>, 973–982 (2007).</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.