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Chemobrionics Database
<p>An online live version of this database can be found in the following webpage: https://cpimentelguerra.com/chemobrionics/</p> <p> </p> <p> </p> <p><em>Acknowledgements</em></p> <p>The authors would like to acknowledge the contribution of the European COST Action CA17120 supported by the EUFramework Programme Horizon 2020.</p> <p>Carlos Pimentel has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101021894 [CARS-CO2]. (02/2022-01/2024)</p>
Canterbury Museum (CMNZ) collection insect specimen-plant flower interactions
<p>This dataset compromises insect-plant flower interactions recorded from the entomology collections of Canterbury Museum, New Zealand (CMNZ). All invertebrate records were extracted from the Museum Vernon database. This included field collection metadata indicating if an insect and plant flower interaction had occurred. A large proportion of records are specimens collected as part of research by Richard Primack in the 1970s, from insects collected from flowering inflorences. Data was cleaned using OpenRefine v.3.6.2. Insect and plant names were reconciled using the GlobalNames extension in OpenRefine. Data was prepared for submission to the Global Biotic Interaction (GloBI) network.</p> <p>This data makes part of a paper submission to the Journal of Applied Entolomolgy Call for Papers on neglected insects pollinators. If accepted this publication will be linked to this dataset.</p> <p>This version included name updates for insect species, spreadsheet data used to produce summary statistics for the manuscript submission and the README file.</p>
Datafile for HERMES code
<p>This repository includes the data files for the code <a href="https://github.com/cosmicrays/hermes">HERMES</a>.</p> <p>The code is described in <a href="https://ui.adsabs.harvard.edu/abs/2021A%26A...653A..18D/abstract">Dundovic et al., 2021, A&A, 653, A18, arXiv:2105.13165</a> </p>
MethylDetectR - A Translational Tool for Methylation-Based Health Profiling
<p><strong>** CORRECTION (2025-05-29): Please note that the script <a href="https://zenodo.org/api/records/15548022/draft/files/Script_For_User_To_Generate_Scores.R/content" target="_blank" rel="noopener noreferrer">Script_For_User_To_Generate_Scores.R</a> had an error whereby single-CpG EpiScores were producing the same score for all samples. This has been corrected in the newest version. </strong><br><br>This dataset includes reproducible code for the two applications related to the 'MethylDetectR' software. These .R files are included as 'MethylDetectR - Calculate Your Scores.R' and 'MethylDetectR.R'. An example DNAm file and SexAgeinfo file for upload to 'MethylDetectR - Calculate Your Scores' are included. These are 'DNAm_File_Example.rds' and 'SexAgeinfo_example.csv' respectively. An example output file from this application/for upload to 'MethylDetectR' is included as 'MethylDetectR - Test For Upload.csv'. An example and optional input file for case/control data is also available as 'MethylDetectR_Case_Control_Example.csv'. </p> <p>Furthermore, a script for the user to generate their own DNAm-based estimated values for human traits is included as 'Script_For_User_To_Generate_Scores.R'. A necessary associated file as 'Predictors_Shiny_By_Groups.csv' is also present for the script to run. We have also included separate necessary files to generate the chronological age predictor from Bernabeu <em>et al.</em> in our most recent versions of MethylDetectR. </p> <p>Lastly, an additional file called 'Truncate_to_these_CpGs.csv' is available which allows users to subset their methylation file to those CpG sites used in the 'MethylDetectR - Calculate Your Scores' application. This may substantially reduce the size of the methylation file for upload as well as its upload time. </p>
Time-series of shoreline change along the Pacific Rim
<p>This repository contains 40 years of tidally-corrected shoreline change time-series for most sandy coastlines around the Pacific Rim derived from Landsat imagery. <br><br><strong>The time-series were last updated in May 2025. For the latest data always refer to <a href="http://coastsat.space/">http://coastsat.space/</a>.</strong></p> <p>The dataset was used to investigate the impact of ENSO on beach erosion and accretion in:<br>- Vos, K., Harley, M.D., Turner, I.L. <em>et al.</em> Pacific shoreline erosion and accretion patterns controlled by El Niño/Southern Oscillation. <em>Nat. Geosci.</em> <strong>16</strong>, 140–146 (2023). <a href="https://doi.org/10.1038/s41561-022-01117-8">https://doi.org/10.1038/s41561-022-01117-8</a><em> </em></p> <p><em>CoastSat </em>was used to map shoreline changes on Landsat 5, Landsat 7 and Landsat 8 imagery between 1984 and 2025. The <em>Coastsat </em>toolbox is publicly available at https://github.com/kvos/CoastSat and described in <em>Vos et al. 2019, </em><a href="https://doi.org/10.1016/j.envsoft.2019.104528">https://doi.org/10.1016/j.envsoft.2019.104528</a>. The time-series of shoreline change were tidally-corrected along cross-shore transects using tide levels from a global tide model (FES2022) and a satellite-derived estimate of the beach slope (as described in <em>Vos et al. 2020, "Beach slopes from satellite-derived shorelines", </em><a href="https://doi.org/10.1029/2020GL088365">https://doi.org/10.1029/2020GL088365</a><em>)</em>.</p> <p>This dataset covers wave-dominated sandy coasts in the Pacific basin where Landsat imagery was available, including a total of 3,000 beaches and more than 100,000 cross-shore transects (100-m alongshore spaced). This includes coastlines in Australia, New Zealand, Japan, Chile , Peru, Mexico and USA (California and Hawaii only).</p> <p>The data is structured as follows:</p> <ul> <li>There is a folder for each country (e.g. Australia)</li> <li> In the country folder, there is a folder for each site (e.g. aus0001, aus0002 etc)</li> <li>In the site folder, there are 4 CSV files: <ul> <li><em>time_series_tidally_corrected.csv</em>: this file contains the tidally-corrected time-series of shoreline change along each transect belonging to the site (e.g. aus0001-0001, aus0001-0002 etc). This is the final product used for coastal change analyses.</li> <li><em>time_series_raw.csv</em>: this file contains the raw time-series of shoreline change, which have not be tidally-corrected. Note that each image is taken at a different stage of the tide.</li> <li><em>tide_levels_fes2022</em>: this file contains the tide levels at the time of image acquisition extracted from FES2022 (global tide model publicly available on AVISO+).</li> <li><em>transect_coordinates_and_beach_slopes.csv</em>: this file contains the coordinates (in WGS84 lat/lon coordinates) as well as the estimated beach slope for each transect, including confidence intervals.</li> </ul> </li> </ul> <p> In addition, there are four geospatial layers (.GEOJSON) which contain important spatial information:</p> <ul> <li> <em>polygons.geojson</em>: this layer contains the polygons that were used to run CoastSat for each beach.</li> <li><em>shorelines.geojson</em>: this layer contains the sandy shorelines that were used to generate the cross-shore transects (also used as reference shorelines in CoastSat). Each beach has the following attributes: beach length, median orientation, median slope, and mean springs tidal range.</li> <li><em>transects.geojson</em>: this layer contains the cross-shore transects, which are spaced 100 m along each beach. Each transect has the following attributes: orientation, beach slope, linear trend (in m/year), alongshore distance relative to the northern end of the beach (absolute and normalised).</li> <li><em>transects_edit.geojson</em>: this layer is the same as transects.geojson but the transects that are not suitable for shoreline mapping were manually deleted (rocky shores, submerged reef, coastal lagoons and inlets, coastal defences etc...).</li> <li><em>transects_ENSO.geojson</em>: this layer (similar to transects.geojson) contains the transects that were used to analyse ENSO effects on shoreline changes in the Pacific (a total of 83,000).</li> </ul> <p> </p>
Dataset of "Cobalt and nickel doped WSe2 as efficient electrocatalysts for water splitting and as cathodes in hydrogen evolution reaction PEM water electrolysis"
<p>Efficient electrocatalysts are crucial for water splitting and fuel cells. Using cheap alternatives that can improve reaction kinetics is essntial for advancing fuel cell technology. Although, tungsten diselinide (WSe2) is promising for electrocatalysis is not fully explored, especially in oxygen evolution and in applications such as polymer electrolyte membrane water electrolyzer.<br>In this work, we used a simple approach to dope WSe2 with cobalt and/or nickel atoms. The doped material was subsequently tested for hydrogen evolution reaction and oxygen evolution reaction. Accordingly, the two electrocatalysts are highly active and stable, affording low overpotentials comparable to those of noble metals. The effective introduction of heteroatoms causes the retention of coordination vacancies, furnishing active catalytic sites that enhanced electrocatalytic performance both in activity and charge transfer. Moreover, both doped materials show excellent performance and stability as cathode electrocatalysts in the polymer electrolyte membrane water electrolyzer with great promise for real-world applications.</p>
TCOM-CFC11 : TOMCAT CTM and Occultation Mesurement based daily zonal mean CFC-11 stratospheric profiles constructed using machine leaning (2000-2023)
<h3>TOMCAT CTM and Occultation measurement-based Stratospheric CFC11 (TCOM-CFC11) profile data data set </h3> <h3>Sandip S. Dhomse </h3> <p>School of Earth and Envio, University of Leeds, Leeds, UK</p> <p>National Centre for Earth Observations, University of Leeds, Leeds, UK</p> <p> email: s.s.dhomse@leeds.ac.uk</p> <p> </p> <h3>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC11 (CFCl3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-11 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.</h3> <h3>Dataset also includes two files containing daily mean zonal mean CFC11 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (8766 days/64 latitudes):</h3> <h3>zmcfc11_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km)</h3> <h3>zmcfc11_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa)</h3> <h3>Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.</h3> <h3> </h3> <h3> </h3>
Dataset of "Electronic structure and defect states in bismuth and antimony sulphides identified by energy-resolved electrochemical impedance spectroscopy"
Understanding the nature of the defects in the absorber materials, namely point defects, their formation mechanism and the contribution to the properties is essential for the photovoltaic device performance improvement. They are one the reasons why chalcogenide-based solar cells do not yet meet expected high power conversion efficiencies. Here we identify and present energy distribution of defects in Bi2S3 and Sb2S3, and their (SbxBi(100-x))2S3 alloys (with x = 0, 10, 33, 50, 67, 90, 100 at% Sb content) chalcogenides, being explored for emerging photovoltaic applications as they are earth-abundant and highly absorbing in the visible light range. We show that their density of states (DOS) and related parameters can be obtained experimentally by energy-resolved electrochemical impedance spectroscopy (ER-EIS) in a technically simple and quick way, where ER-EIS data are well correlated with theoretical DFT calculations. ER-EIS reveals that in Bi2S3 there are only shallow defects at CBM. In Sb2S3, ER-EIS reveals also midgap states which can be the cause of low electrical conductivity of Sb2S3. We also explain the discrepancy in the reported values of ionisation potentials and the bandgaps of the Bi- and Sb-chalcogenides. Dominant sulphur vacancy defect was identified in Bi- and Sb-chalcogenides whereas in ternary (SbxBi(100-x))2S3 system, merely 10 at.% of Bi transforms the midgap sulphur defects to shallow ones. This provides novel strategy for healing the midgap defects in Sb2S3, which is crucial for boosting the PV performance and tuning the electrical conductivity in Sb2S3.
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Data for: Cephalopod Sex Determination and its Ancient Evolutionary Origin
<p>This repository contains the chromosome-level genome assembly, annotation, and genome hub files of the California two-spot octopus (<em>Octopus bimaculoides</em>). These data resulted in the discovery of the cephalopod sex chromosomes. Read the paper in <em>Current Biology </em>here: https://doi.org/10.1016/j.cub.2025.01.005. </p>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, derived from national census and EU Copernicus datasets
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p>
ABG-IMRHT and Ames-2000K IR line lists for N2O as reported in "Accurate N2O IR Line Lists with Consistent Empirical Line Positions: ABG-IMRHT and Ames-2000K"
<p><strong>[2025-06-17, v2.0</strong>, updated from v1.1 (<a href="https://doi.org/10.5281/zenodo.14834513">10.5281/zenodo.14834513</a>)]<br>(1). This upgraded version is recommended for analysis involving B1b or ABG(-IMRHT) related line lists, or E' > 10,000 cm<sup>-1</sup>. <br>(2). Energy level lists computed on the B1b PES and related 296K ABG-IMRHT line intensity and line lists, and B1b-based 1000-3000K line list files have been updated, along with n2olist.f90.v1.4. <br>(3). The full 296 K IR line lists of <strong>all 12 stable isotopologues</strong> are provided in "n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz", in which the line intensities assume 100% abundance for every isotopologue.<br>(4). Please note that the B1b PES-based energy levels in "empirical.corrections.for.ABG-IMRHT.zip" are still valid, but the B1b PES based energy levels are not updated in the files inside that .zip. </p> <p>[<strong>Link to <a href="https://www.sciencedirect.com/science/article/pii/S0022407325001645">the N2O paper</a> </strong>published at JQSRT (2025) 343, 109502, part of VSI:HITRAN2024, doi:<a href="https://doi.org/10.1016/j.jqsrt.2025.109502">10.1016/j.jqsrt.2025.109502</a>]</p> <p>[Updated from v1.0 (<a href="https://doi.org/10.5281/zenodo.14174307">10.5281/zenodo.14174307</a>). The J=151-210 transitions of <sup>14</sup>N<sub>2</sub><sup>16</sup>O were missing from v1.0 files ]</p> <p><strong>1. Second generation of Ames-296K IR line list for "natural" Nitrous Oxide (N<sub>2</sub>O), denoted ABG-IMRHT</strong>, which was computed from Ames-B1b PES refinement (using Benjamin Schröder's ab initio PES Comp I, doi:10.1515/zpch-2015-0622) and 2023 dmsG-10K accurately fitted from CCSD(T)/aug-cc-pV(T,Q,5)Z dipoles. In this major upgrade to Ames-296K N<sub>2</sub>O line list (10.1080/00268976.2023.2232892 and 10.5281/zenodo.7888194), rovibrational energy levels computed from NOSL-296 EH model were adopted to match and replace ~100,000 <sup>14</sup>N<sub>2</sub><sup>16</sup>O levels. More consistent empirical corrections are determined for multiple isotopologues from comparison with RITZ (IAO), MARVEL (ExoMol), HITRAN, and JPL datasets. The ABG-IMRHT line list provides the most reliable and consistent IR intensity predictions and accurate line positions in the range of 0 - 10,000 cm<sup>-1</sup>. All 12 stable isotopologues are included. </p> <p><strong>2. Ames-2000K</strong> IR line list provides complete, reliable and consistent IR predictions in the 0 - 15,000 cm<sup>-1</sup> range. It includes transitions of 12 isotopologues. Their intensities are scaled by corresponding terrestrial "natural" abundances. Ames-2000K is a composite list, including 4 component lists, to achieve better accuracy and reliability needs at both shorter and longer wavelengths: </p> <ul> <li>#1. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-1 PES and 2023 dmsG-wgt2d, J'<210, E'<25,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K. It occupies 24 GB in compressed .xz format. </li> <li>#2. 1000 K line lists of #2-#12 minor isotopologues, computed on Ames-1 PES and DMS, J'<150, E'<0.125 au - zpe (iso 2-6) or 0.08 -0.10 au - zpe (iso 7-12), S<sub>1000K </sub>> 10<sup>-34</sup> cm/molecule, size-reduction with 99.9% intensity conservation in cm<sup>-1</sup> bins. </li> <li>#3. a hot line list of <sup>14</sup>N<sub>2</sub><sup>16</sup>O, computed on Ames-B1b PES and 2023 dmsG-10Kcm<sup>-1</sup>, J'<150, E'<16,000 cm<sup>-1</sup>, T=1000 / 1500 / 2000 / 3000 K.</li> <li>#4. ABG-IMRHT IR line list at 296 K, J'<150, E'<16,000 cm<sup>-1</sup>, with best empirical line positions and highly consistent intensity predictions up to 10,000 cm<sup>-1</sup>. Coverage beyond 10,000 cm<sup>-1 </sup>is limited to strong lines.</li> </ul> <p><strong>3. List of files:</strong> (decompress .xz files first, "xz -dkf -T0 file.xz")</p> <ul> <li>IAO_N2O_levels.tar.xz: rovibrational energy levels of 6 N<sub>2</sub>O isotopologues, as computed using global Effective Hamiltonian (EH) models developed by Dr. Sergey Tashkun from IAO (Institute of Atmospheric Optics, Tomsk, Russia, <a href="https://www.iao.ru/">https://www.iao.ru/</a>). <br><br></li> <li>N2O.Ames-B1b.PES.and.Ames-2023.DMS.zip: Ames-B1b PES subroutine & coefficient file, see the note in n2opes2.f90; Ames 2023 dmsG subroutine and coefficients for fits up to 10K/12K/15K/17K/20K/25K cm<sup>-1</sup>, along with fitting residuals and compared to Ames-1 style (dmsC) coeffs and residuals. The geometry set has ~80 points in each 100 cm<sup>-1</sup>. <br><br></li> <li>n2olist.f90.v1.4: the main Fortran program for Ames-2000K generation, customizable, see the note at its beginning. <br>[ default Ames-2000K = Ames-1 (12 iso) + [B1b (446) + ABG-IMRHT (12 iso)] if (E'<15,000 cm-1 & J<=150) ]<br><br></li> <li>empirical.corrections.for.ABG-IMRHT.zip: subroutine and paired/corrected energy level lists used in the empirical correction of energy levels computed on B1b PES [<em>this file is not updated from v1.1 to v2.0</em>]<br><br></li> <li>n2o.partition.1-4000K.iso.1-12.scaled: partition sum for 12 isotopologues, input file required by n2olist.f90, excluding g_n<br>n2o.partition.1-4000K.iso.1-12.scaled.with.degeneracy.txt: same as above, g_n included, see the note inside<br><br></li> <li>list.of.n2o.xz.files : list of .xz files to read/regenerate from, under subdir "xz", input file required by n2olist.f90<br>ames.n2o.xx000-yy000.cm-1.xz: compressed data files for Ames-2000K (line list component #1+#2)<br><br></li> <li>n2o.iso1-12.levels.Ames-1.dat.xz: energy levels of 12 N<sub>2</sub>O isotopologues on Ames-1 PES, input file required by n2olist.f90<br><br></li> <li>n2o.446.B1b-PES.levels.xz: energy levels of <sup>14</sup>N<sub>2</sub><sup>16</sup>O computed on Ames-B1b PES, input file to n2olist.f90<br><br></li> <li>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.compressed.xz: data file for <sup>14</sup>N<sub>2</sub><sup>16</sup>O hot list on Ames-B1b and dms 2023-G10K (component #3), input file required by n2olist.f90<br>n2o.446.B1b-dmsG10K.1000K-3000K.Eup16K.0-10Kcm-1.dat.xz: independent line list (component #3)<br><br></li> <li>n2o.iso1-12.levels.ABG-IMRHT.dat.xz: energy levels in ABG-IMRHT list, with empirical corrections included, input file required by n2olist.f90</li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5.corrected.xz: Latest Ames-296K line list with empirical energy level corrections (component #4), input file for n2olist.f90<br>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5.corrected.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>n2o.296K.ABG-IMRHT.20250602.dat.v5corrected.100pct-abundance.xz: Latest Ames-296K line list with empirical energy level corrections, including the full set of IR transitions for <strong>12 stable isotopologues, assuming 100% abundannce </strong>for every isotopologue, and 1E-31 cm/molecule intensity cut-off at 296 K.</li> <li>n2o.296K.ABG-IMRHT.20250602.with.broadening.dat.v5corrected.100pct-abundance.xz: same as above, in HITRAN format, including line-broadening parameters<br><br></li> <li>ames.n2o.intensity.xz: line count and intensity sum (original, selected, iso #1 and iso #2-12) in each 0.01 cm<sup>-1</sup> bins at 296 K, 1000 K, 1500 K, 2000 K, and 3000 K, for reference and statistics, optional input file to n2olist.f90<br><br></li> <li>ames.n2o.sint.reductions.xz: A check-point file during Ames-2000K file generation, for reference only. including # of lines (total & selected), intensity sum (total, iso #1, iso #2-12), and intensity retention ratio in each 0.01 cm<sup>-1</sup> bins for total, iso #1, and iso #2-12.<br><br></li> </ul> <p><strong>4. List of 12 isotopologues</strong>, abundances adopted, and number of lines in ABG-IMRHT and Ames-2000K line list, <br>[updated 2025-06-17 v2.0: the #lines in ABG-IMRHT are updated to S(296K) cut-off at 1E-31 cm/molecule, instead of 5E-31]</p> <table> <tbody> <tr> <td>#</td> <td>ISO</td> <td>abundance</td> <td># lines in ABG-IMRHT</td> <td># lines in Ames-2000K</td> </tr> <tr> <td>1</td> <td>446</td> <td>0.990333</td> <td>1388017</td> <td>3014643103</td> </tr> <tr> <td>2</td> <td>456</td> <td>3.64093E-3</td> <td>375541</td> <td>97932924</td> </tr> <tr> <td>3</td> <td>546</td> <td>3.64093E-3</td> <td>411353</td> <td>118432059</td> </tr> <tr> <td>4</td> <td>448</td> <td>1.98582E-3</td> <td>377552</td> <td>88169721</td> </tr> <tr> <td>5</td> <td>447</td> <td>3.69280E-4</td> <td>238921</td> <td>40212389</td> </tr> <tr> <td>6</td> <td>556</td> <td>1.33858E-5</td> <td>93933</td> <td>7534736</td> </tr> <tr> <td>7</td> <td>548</td> <td>7.30080E-6</td> <td>93674</td> <td>5583963</td> </tr> <tr> <td>8</td> <td>458</td> <td>7.30080E-6</td> <td>86446</td> <td>4995485</td> </tr> <tr> <td>9</td> <td>547</td> <td>1.35765E-6</td> <td>55360</td> <td>2588347</td> </tr> <tr> <td>10</td> <td>457</td> <td>1.35765E-6</td> <td>50754</td> <td>2275332</td> </tr> <tr> <td>11</td> <td>558</td> <td>2.68412E-8</td> <td>15814</td> <td>500190</td> </tr> <tr> <td>12</td> <td>557</td> <td>4.99134E-9</td> <td>8537</td> <td>274623</td> </tr> <tr> <td> </td> <td>Total</td> <td>1.00000069</td> <td>3195902</td> <td>3383142872</td> </tr> </tbody> </table> <p>5. This project is funded by NASA Grant 18-APRA18-0013 through NASA/SETI Institute Co-operative Agreement 80NSSC20K1358. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</p>
Harmonized IACS inventory
<h2>Inventory description</h2> <p>The <strong>Harmonized IACS inventory of Europe-LAND</strong> is a harmonised collection of data from the Geospatial Aid (GSA) system of the Integrated Control and Administration System (IACS), which manages and controls agricultural subsidies in the European Union (EU). The GSA data are a unique data source with field-levels of land use information that are annually generated. The data carry information on crops grown per field, a unique identifier of the subsidy applicants that allows to aggregate fields to farms, and information on organic cultivation.</p> <p>The inventory contains all data that can be shared following the General Data Protection Regulations (GDPR) of the data providers. <span lang="EN-GB">It covers 19 EU member states with time series up to 17 years. For most members states, only the crop information can be shared. However, for six member states also the </span><span lang="EN-GB">farm identifier (Czechia, Denmark, Estonia, Ireland, Portugal and Spain)</span><span lang="EN-GB"> and for five also the </span><span lang="EN-GB">organic management information (Austria, Flanders in Belgium, Denmark, Ireland, and Bulgaria)</span><span lang="EN-GB"> can be shared.</span> </p> <p>Due <span lang="EN-GB">to General Data Protection Regulations (GDPR), </span><span lang="EN-GB">we are not allowed to share all data</span><span lang="EN-GB"> that we collected and harmonised. We hold GSA data for six additional member states (Italy, Greece, Poland, Hungary, Romania, and Cyprus) as well as supplementary information on farm-level indicators for 17 additional member states, federal states, or regions (Austria, Cyprus, Greece, Latvia, Netherlands, Romania, Sweden, Slovenia, Slovakia, Wallonia in Belgium, Brandenburg, Lower Saxony, Saxony-Anhalt, and Thuringia in Germany, and Emilia-Romagna, Marche, and Toscana in Italy,) and organic farming information for seven more member states, federal states, or regions (Greece, Netherlands, Sweden, Slovenia, Slovakia, Wallonia in Belgium, and Brandenburg, Lower Saxony, Saarland, Saxony-Anhalt, and Thuringia in Germany). For Luxembourg and Malta, only LPIS data were available. As these datasets contain only reference parcels without detailed land-use information at the parcel level, they were not included in the inventory.</span> </p> <p>I<span lang="EN-GB">f you use the data, please also </span><span lang="EN-GB">cite the original sources of the data</span><span lang="EN-GB">. You can find the references in the </span><span lang="EN-GB">documentation provided</span><span lang="EN-GB"> </span><span lang="EN-GB">in the "_Documentation.zip".</span> </p> <p>The crop information were harmonised using the <strong>Hierarchical Crop and Agriculture Taxonomy (HCAT) </strong>of the <a href="https://zenodo.org/records/14094196" target="_blank" rel="noopener">EuroCrops</a> project (<a href="https://doi.org/10.1038/s41597-023-02517-0" target="_blank" rel="noopener">Schneider et al., 2023</a>). To allow for interoperability with EuroCrops, the harmonised Europe-LAND data come with the same column names that relate to the crop information. All crop mapping tables can be found in our <a href="https://github.com/clejae/europe_land_iacs_prep">GitHub repository</a>.</p> <h3>Column names:</h3> <ul> <li>field_id (mandatory): Unique identifier for each parcel per member state, state, or region</li> <li>farm_id (optional): Unique identifier for each farm per member state, state, or region</li> <li>crop_code (mandatory): Original, member state-specific crop code</li> <li>crop_name (mandatory): Original, member state-specific crop name</li> <li>EC_trans_n (mandatory): Original crop name translated into English</li> <li>EC_hcat_n (mandatory): Machine-readable HCAT name of the crop</li> <li>EC_hcat_c (mandatory): The 10-digit HCAT code indicating the hierarchy of the crop</li> <li>organic (optional): Whether a parcel was conventional (0), organic (1), or is in the conversion process to organic cultivation (2)</li> <li>field_size (mandatory): Size of parcel/reference parcel in hectares</li> <li>crop_area (optional): Area in hectares of the main crop reported in crop column. The crop_area column only occurs if multiple crops are reported per reference parcel.</li> </ul> <p>M<span lang="EN-GB">ore detailed information for all members states in our harmonised inventory can also be found in the documentation.</span> </p> <div> <p><span lang="EN-GB">The inventory will be updated at least annually</span><span lang="EN-GB">. We will update as additional data becomes available and as new versions of the HCAT are released. Moreover, in future versions, we will add new data on information on agri-environmental measures, eco-schemes, and animal numbers per farm. </span> </p> </div> <h2>Information on data provision</h2> <p><strong>Al<span lang="EN-GB">l files come as .geoparquets</span></strong><span lang="EN-GB"> to stay within the space limitations of Zenodo. Geoparquets can simply be opened in QGIS via drag and drop. Additionally, various libraries from different porgramming languages are able to handle geoparquets, e.g. geoarrow and sgarrwo in R, GDAL/OGR in C++, GeoParquet.jl in Julia or Fiona in Python.</span> </p> <div> <p><span lang="EN-GB">We bundled multiple years of each member state to stay below the file number limitation of Zenodo. Each zip file name indicates the member state, federal state, or region and the years covered. The meaning of the abbreviations of the members states, federal states, and regions can be found in the "country_region_codes.xlsx" in the "_Documentation.zip". </span> </p> </div> <div> <p><span lang="EN-GB">The Spanish data are also bundled across regions, as they are separated into 50 regions. See the country_regions_codes.xlsx tables for the meaning of the abbreviations:</span> </p> </div> <ul> <li>ES_Bundle1 (Northeast): BAL, BAR, CAS, GIR, HEC, LLE, NAV, TAR, TER, ZAR</li> <li>ES_Bundel2 (Northwest): ACO, ALA, AST, BUR, CAN, GUI, LEO, LRI, LUG, OUR, PAL, PON, VIZ, VLD, ZAM</li> <li>ES_Bundle3 (West): ALB, AVI, CAC, CIU, CUE, GUA, MAD, SAL, SEG, SOR, TOL</li> <li>ES_Bundle4 (Southwest): ALI, ALM, BAD, CAD, CDB, GRA, HEV, JAE, LAP, MAL, MUR, SAN, SEV, VLC</li> </ul> <h2>Changelog</h2> <p><strong>V</strong><span lang="EN-GB"><strong>ersion 1.2:</strong> </span><span lang="EN-GB">In this version, we have corrected the inventory description and documentation to only refer to data that we share publicly. Additionally, we corrected an error in the Spanish data, where the crop_code column got mixed up during pre-processing. This did not affect the other columns, which were correct. Moreover, we have uploaded new data for Bulgaria to the inventory.</span> </p> <p><strong>Version 1.1:</strong> In this version, we corrected some data errors that occured in v1 due to a failure of our quality checks. First, not all fields got classified in v1, and secondly, there were two different datatypes in the EC_hcat_c in many files. Both errors are now corrected.</p>
AusTraits Plant Dictionary (APD)
<p>The Austraits Plant Dictionary (APD) offers detailed descriptions for more than 500 plant trait concepts.</p><p>APD includes trait focused on plant morphology, plant nutrient concentrations, plant physiology, plant life history, and plant fire response. The definitions will be useful to researchers from diverse disciplines, including plant functional ecology, plant taxonomy, and conservation biology. All trait concepts are supported by comprehensive metadata including trait descriptions, allowable trait values, allowable ranges, preferred units, keywords, references, and links to matches in a selection of trait databases. The traits describe here also fully support the AusTraits plant trait database, doi.org/10.5281/zenodo.3568417.</p><p>The APD can be viewed online at:</p><ul><li>https://w3id.org/APD</li><li>https://vocabs.ardc.edu.au/viewById/649 (Research Vocabularies Australia)</li></ul><p>The project GitHub repository is at:</p><ul><li>https://github.com/traitecoevo/APD</li></ul>
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
S0 | SUSDAT | Merged NORMAN Suspect List: SusDat
<p>This is the collection associated with list S0 SUSDAT on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a> and <a href="https://www.norman-network.com/nds/susdat/">https://www.norman-network.com/nds/susdat/</a></p> <p>S0 SUSDAT <strong>Merged NORMAN Suspect List: SusDat</strong></p> <p><a href="https://www.norman-network.com/nds/susdat/">Interactive Data table</a> (csv of latest version included in dataset here)</p> <p>UPDATED <strong><em>Jun 3, 2025 to latest version</em></strong>. Compiled by Reza Aalizadeh, University of Athens, including RTI and toxicity values, support by Nikiforos Alygizakis, EI. <em>Work in progress ... please report any issues!</em></p> <p>For an explanation of column headers, please see metadata files (xlsx and csv). For curation notes, see "SusDat_curation_notes.txt". </p>
Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Gobabeb Site in Namibia
<p>The HYPERNETS project (www.hypernets.eu; Ruddick et al. 2024) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical satellite products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu; Kuusk et al. 2024) dedicated to land and water surface reflectance validation with instrument pointing capabilities. This instrument has been deployed over various sites covering a range of water and land types and a range of climatic and logistic conditions. Here, we provide the first fully quality-checked data for the Gobabeb HYPERNETS site in Namibia (GHNA). The HYPERNETS data products were processed using the HYPERNETS_processor (De Vis et al. 2024b).</p> <p>The provided NetCDF files are the L2B hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in these products is the Hemispherical-conical Reflectance Factor (HCRF) defined as: HCRF = π L / E where L is the conical upwelling radiance (with field of view of 5 degrees) and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The GHNA site has minimal daily variation in surface cover and weather conditions and is an ideal location for sustained, homogeneous measurements. The site is well characterised as it is very close to an instrument already recognised as a radiometric calibration site (GONA) as part of the RadCalNet network (Bialek et al. 2016). The HYPERNETS site itself (23.60153 degrees S, 15.12589 degrees E) is 650 m from the RadCalNet site, and is located on a gravel plain near a dry riverbed which separates it from the neighbouring dune sea. The HYPSTAR®-XR sensor was installed May 2022 at the top of a 9m mast on an extended 1 m horizontal boom to minimise interruption of the field of view. Data are collected every 30 minutes between 9am and 6pm local time (UTC+02) between viewing zenith angles of 0 and 60 degrees. No measurements are taken at 2pm and 2:30pm local time to avoid the hottest part of the day.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (De Vis et al. 2024b) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All of the products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). For an example using these data for satellite vicarious calibration, see De Vis et al. 2024a).</p> <p>To obtain this dataset, we start from the full GHNA data record and omit data that does not pass the relevant quality checks (QC). Some QC are performed during the near-real time processing done by the hypernets_processor (see https://hypernets-processor.readthedocs.io/en/latest/content/atbd/processing/quality_checks.html) to produce the L2A files. Then, a number of site-specific QC are performed as post-processing to produce the L2B files. These site-specific QC cover things such as removing flags in the L2A data, avoiding periods with bad deployment conditions, removing unsuitable viewing and solar angles, as well as poorly performing wavelength ranges and individual sequences. Any potential misalignment of the sensor is also corrected, affecting L1D irradiances, and L2B reflectances. These corrected data are then used in a more stringent clear sky check, and in a check that verifies the reflectances are within realistic ranges for a given angle and time of year for the given site. </p> <p>There was a rain event in Gobabeb in March 2025, resulting in the growth of grass at the site. We expect the site will be back to its normal surface cover in the near future. Since the rain event, less data passed the site-specific QC. A dedicated QC will be developed for this period, as the data with grass surface cover will still be useful for satellite validation. These updated data will be made available in the future. </p>
S3 | NORMANCT15 | NORMAN Collaborative Trial Targets and Suspects
<p>This is the collection associated with list S3 NORMANCT15 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S3 | NORMANCT15 | <strong>NORMAN Collaborative Trial Targets and Suspects</strong></p> <p>Schymanski <em>et al</em>. 2015.<br>DOI: <a href="http://link.springer.com/article/10.1007/s00216-015-8681-7">10.1007/s00216-015-8681-7</a></p> <p>Upload 22/3/2020: added merged InChIKey file for PubChem data extraction. 27/6/2025: added merged CSV</p>
Dataset of "Towards 2D van der Waals Entropy Mixture MX2 (M=Mo,W; X=S,Se,Te) for Hydrogen Evolution Electrocatalysis"
<p>High-entropy alloys have emerged as a class of materials, offering unique properties due to their irregular and randomized arrangement of multiple elements in an ordered lattice. This concept has been extended to two-dimensional (2D) van der Waals materials, including transition metal dichalcogenides (TMD), which exhibit promising applications in electrocatalysis. In this work, we have explored the synthesis of entropy mixture crystals (TMDmix) involved the chemical vapor transport of five individual elements, Mo and W as metal elements, S, Se, and Te as chalcogenide elements, resulting in a crystalline structure with a controlled composition Mo0.56W0.44(S0.33Se0.35Te0.32)2, with an estimated ΔSmix of 0.96R. When observed along the [001] zone axis, STEM HAADF images indicate the presence of the different crystal phases of the 2D TMDs (1T, 2H, and 3R). Our findings demonstrate the potential of the entropy TMDmix materials as catalysts for the hydrogen evolution reaction, as an alternative to noble metal-based catalysts. To maximize the potential of TMDmix, we chose the chemical exfoliation with the resulting material being subdivided into size groups, big and small according to their lateral size. In acidic medium, the lowest overpotential of 127 mV and Tafel slope of 79 mV/dec were obtained for the exfoliated sample with a small lateral size (exf-TMDsmall).</p>
ScienceDex guides
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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