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Dataset of "High Entropy 2D Metals Sulfides: Fast Synthesis, Exfoliation and Electrochemical Activity in Overall Water Splitting at Alkaline pH"
<p>Novel simple and efficient method for synthesis of high entropy sulfides of iron group metals (Cr, Fe, Ni, Co, Zn) is describedThe created material was investigated as a catalyst for electrochemical water splitting in acidic, neutral and alkaline pH. Investigation of the electrocatalytic activity of the synthesized material shows its high efficiency for overall water splitting in alkaline media. </p>
Dataset for Gate-to-Gate Life Cycle Assessment of Lithium-Ion Battery Recycling Pre-Treatment
<p>Recycling spent lithium-ion batteries (LIBs) is crucial for improving environmental sustainability and conserving resources. Due to the diversity of LIB applications and recycling technologies, the environmental and energy impacts are not well understood. Comprehensive assessments must consider the distinct operations, methodologies, technology efficiency, and final treatment of materials. This study provides a partial gate-to-gate life cycle analysis (LCA) of a small-scale recycling plant in the Czech Republic, focusing on pre-treatment of spent LIBs from electric vehicles (EVs) and consumer electronics cells (CECs). The study highlights the benefits of recycling pre-treatment for CECs, significantly reducing environmental impact categories (EICs) such as climate change, eutrophication, and resource use. A high secondary use rate of obtained materials is crucial for environmental benefits, with metal reuse from packaging, connectors, and current collectors being especially important.</p>
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Arctic sea ice velocity in summer from AMSR2 (2013-2023)
<p>Sea ice drift in summer plays a key role in Arctic sea ice mass balance and navigation safety of the Arctic Passage. Resulted from surface melt over sea ice and atmospheric water vapor, previous passive microwave sea ice velocity data present relatively poor quality in summer than in winter. Here, based on an improved sea ice velocity retrieval method, we produced daily Arctic sea ice velocity data during summertime (May 1st to September 30th) from 2013 to 2023. These sea ice velocity data are derived from the daily gridded AMSR2 brightness temperature (TB) at 36.5 GHz channel distributed by the University of Bremen using the continuous maximum cross-correlation algorithm. We used the polarization difference of TB to track the displacement of the sea ice templates. The size of templates is 11×11 pixels, and the spatial spacing between adjacent templates is five pixels. The time interval of this data is 24 h, and the spatial resolution is 62.5 km. Outliers were identified and discarded by surface wind (10-m wind derived from ERA5 atmospheric reanalysis) and surrounding sea ice velocity vectors. Vectors over open water areas were discarded by sea ice concentration with 6.25 km distributed by the University of Bremen.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
Regular full triangulations of the 3-dilated 3-simplex
<p>This dataset contains the 21 125 102 regular full triangulations of the 3-dilated 3-simplex, computed by mptopcom. The data is given up to symmetry, i.e. we have one triangulation from every orbit. The file `3d3.dat` is the input file for mptopcom, which was called with the `--regular` and `--full` flags. The output is in the file `3d3_regular_full.result.xz`.</p>
UNFCCC country-submitted greenhouse gas emissions data until 2024-07-05
<p>Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05.</p>
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
Mobile phone data for forests in Szklarska Poreba and Swieradow Forest District
<p><strong>Mobile phone data: </strong>Data were collected for forest in 395 base fields (750 m × 750 m). The scope of data collected covers the period from January 1, 2019 to December 31, 2019. Unique user visits were counted in the base fields. A unique visit to the base field was considered to be a visit that occurred on a specific day in a different time period. There are 5 time periods separated: 6:00 - 10:00, 10:00 - 14:00; 14:00 - 18:00, 18:00 - 22:00, 22:00 - 6:00. Mobile phone data were collected to determine the spatial distribution of social activities in forest areas.The fully anonymized data was acquired from Selectivv. It collects information about mobile phone users (over 20 million users in Poland). The scope of data collected by Selectivv includes: user locations; timestamps; data from applications (350,000 applications) and websites (about 17 million pages), where users consent to data collection for better content profiling.</p> <p> </p> <p><strong>Data description:</strong> type - vector layer, column N - number of visits, coordinate system - 2180</p>
theme-d-Prose 1848-1920
<p>The literary text corpus "theme-d-Prose 1848-1920" is a specialized collection of 1,227 German-language literary prose texts (shortest: 2,048 words; longest: 100,909 words).</p> <p>It is an extended subcorpus of "d-Prose 1870-1920" (<a title="d-Prose 1870-1929" href="10.5281/zenodo.4315208" target="_blank" rel="noopener">Gius/Guhr/Adelmann 2021</a>), which is the source of 804 of its text files, originally taken from the corpus KOLIMO (Herrmann and Lauer 2017), which in turn is based on the repositories Gutenberg-DE (Projekt Gutenberg-DE, Hille & Partner, and Reuters 2017), Deutsches Textarchiv (Geyken et al. 2011), and TextGrid (TextGrid Repository 2020).</p> <p>An additional 423 texts were added from Gutenberg-DE (Projekt Gutenberg-DE, Hille & Partner, and Reuters 2017), the Deutsches Textarchiv (Geyken et al. 2011), and the Wikisource Collections (Wikimedia Stiftung 2023).</p> <div> <div> <div> <p>In terms of social and literary history, the selected publication period (1848-1920) covers a time of many political changes in the German-speaking area with a diverse literary production that can retrospectively be grouped into the following major literary movements and epochs: the end of pre-March and the Biedermeier, Realism, Naturalism, Modernism, and Expressionism. </p> <div> <div> <div> <p>In addition to the nationality of the authors and the years of publication, the texts for "theme-d-Prose 1848-1920" were selected primarily on the basis of the 'non-fictional elements', such as geographical, political, or historical entities, present in their fiction, which were found to share a common spatial and temporal setting of their fictional worlds representing the German-speaking area of the 19th and early 20th centuries, making "theme-d-Prose 1848-1920" to be an extended and thematically specialized "d-Prose 1870-1920" subcorpus.</p> <p>Each text was manually tagged with the markup XML element <title></title> from the TEI standards (TEI Consortium 2022, Burnard, Schöch, and Odebrecht 2021). In addition to that, the indications of volume (e.g. "Buch 1" or "Band 2") and chapter headings or chapter numbers were manually tagged with markup XML elements <section></section> for volumes and <chapter></chapter> for chapters.</p> <p>The text corpus is available as a zipped folder of XML files and has been enriched with metadata in an additional CSV file.</p> <p>The CSV file contains basic metadata about:</p> </div> </div> </div> </div> </div> </div> <ul> <li>the authors: name, pseudonyms, year of birth and death, gender, nationality, authority file identifier (GND ID, Deutsche Nationalbibliothek 2024)</li> <li>the corpus texts: title, number of words, date of first publication, genre (as far as this information is directly documented in the consulted repositories, i.e. in the metadata of d-Prose, or Wikipedia and the consulted literary encyclopedias and literary histories, such as Arnold (2020), Killy and Kühlmann (2008), Stein and Stein (2008), or Brenner (2011))</li> <li>the fiction of the texts: information about the time and place of the fiction, including keywords and quotes taken from the texts</li> </ul> <p> </p> <table> <tbody> <tr> <td><strong>number of texts</strong></td> <td><strong>sum in words</strong></td> <td><strong>average</strong></td> <td><strong>median</strong></td> <td><strong>standard deviation</strong></td> </tr> <tr> <td>1,227</td> <td>34,534,384</td> <td>28,145.38</td> <td>16,971</td> <td>27,036.46</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>total number of authors</strong></td> <td>346</td> <td>100%</td> </tr> <tr> <td><strong>number of female authors</strong></td> <td>89</td> <td>25.7%</td> </tr> <tr> <td><strong>number of male authors</strong></td> <td>257</td> <td>74.3%</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> </td> <td><strong>number of texts</strong></td> <td><strong>percentage of the entire corpus</strong></td> </tr> <tr> <td><strong>by female authors</strong></td> <td>315</td> <td>25.7%</td> </tr> <tr> <td><strong>by male authors</strong></td> <td>912</td> <td>74.3%</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>decade</strong></td> <td><strong>number of texts</strong></td> <td><strong>number of words</strong></td> </tr> <tr> <td><strong>1848-1860</strong></td> <td>137</td> <td>2,382,896</td> </tr> <tr> <td><strong>1861-1870</strong></td> <td>135</td> <td>2,353,938</td> </tr> <tr> <td><strong>1871-1880</strong></td> <td>121</td> <td>3,584,370</td> </tr> <tr> <td><strong>1881-1890</strong></td> <td>162</td> <td>4,684,519</td> </tr> <tr> <td><strong>1891-1900</strong></td> <td>256</td> <td>6,695,539</td> </tr> <tr> <td><strong>1901-1910</strong></td> <td>258</td> <td>8,488,707</td> </tr> <tr> <td><strong>1911-1920</strong></td> <td>158</td> <td>6,344,415</td> </tr> </tbody> </table> <p> </p> <p><strong>Clusters of Subcorpora:</strong></p> <table> <tbody> <tr> <td><strong>cluster name</strong></td> <td><strong>subcorpora</strong></td> <td><strong>number of texts</strong></td> <td><strong>number of words</strong></td> <td><strong>shortest text</strong></td> <td><strong>longest texts</strong></td> </tr> <tr> <td><strong>author gender</strong></td> <td> <p>female authors</p> <p>male authors</p> </td> <td> <p>316</p> <p>912</p> </td> <td> <p>9,938,452</p> <p>24,595,932</p> </td> <td> <p>2,500</p> <p>2,048</p> </td> <td> <p>100,909</p> <p>100,180</p> </td> </tr> <tr> <td><strong>publication date</strong></td> <td> <p>1848-1870</p> <p>1871-1900</p> <p>1901-1920</p> </td> <td> <p>272</p> <p>540</p> <p>416</p> </td> <td> <p>4,736,834</p> <p>14,964,428</p> <p>14,833,122</p> </td> <td> <p>2,201</p> <p>2,316</p> <p>2,048</p> </td> <td> <p>99,954</p> <p>100,780</p> <p>100,909</p> </td> </tr> <tr> <td><strong>text length</strong></td> <td> <p>very short texts</p> <p>short texts</p> <p>medium texts</p> <p>long texts</p> </td> <td> <p>425</p> <p>461</p> <p>196</p> <p>145</p> </td> <td> <p>2,327,416</p> <p>9,250,959</p> <p>10,741,578</p> <p>12,214,431</p> </td> <td> <p>2,048</p> <p>10,029</p> <p>40,001</p> <p>70,208</p> </td> <td> <p>9,959</p> <p>39,878</p> <p>69,387</p> <p>100,909</p> </td> </tr> </tbody> </table> <p> </p>
Dataset of "Characterization of Silicon-based Fibers Prepared by Electrospinning for Potential Li-ion Battery Anodes"
<p>The rapid growth of electric vehicles (EVs) is driven by advances in lithium-ion batteries (LIBs), particularly in anode materials. Graphite electrodes, widely used for their high porosity, conductivity, low weight, and cost-effectiveness, face competition from monocrystalline silicon. Silicon anodes offer higher capacity and energy density, and they are safer because of their nonflammable nature. However, silicon's tendency to expand and contract during cycling presents challenges. This study explores the use of silicon nano- and microfibers to enhance battery stability, addressing these issues effectively.<br>Monocrystalline silicon particles, obtained through milling and sieving, were used as the active component in the nanofibers. These particles, combined with organic precursors (PVP and TEOS), were processed using electrospinning to form fibers. The fibers were then annealed at 650 °C to remove the polymeric PVP component. <br>The results provide valuable insights into the properties and interactions of the silicon nanofibers, highlighting their potential in advanced energy storage devices. </p>
Dataset of "Spinel Glass Fibers for Application in Solid-State Batteries"
<p>All-solid-state batteries are currently considered one of the most reliable battery systems, as they are nonflammable and do not form dendrites during the charging cycle, unlike liquid electrolyte batteries. Additionally, they are expected to offer higher energy density compared to current batteries. This work focuses on the preparation of a material that could serve as a cathode in all-solid-state batteries. The cathode material was obtained from end-of-life batteries, which enabled its reuse, and was used as a filler in glassy nano/micro-fibers prepared by electrospinning. Using the material in the form of nano/micro-fibers is expected to shorten the diffusion lengths of lithium ions, increase capacity, and improve the flexibility and stability of the material during cycling.</p>
Data and code for: Rachel A Reeb, J Mason Heberling, & Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. AoB PLANTS, plae058
<p>Data and Analysis Code for: </p> <p>Rachel A Reeb, J Mason Heberling, Sara E Kuebbing (2024). Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity. <em>AoB PLANTS</em>, plae058. <a href="https://doi.org/10.1093/aobpla/plae058">https://doi.org/10.1093/aobpla/plae058</a></p> <p>Includes two R markdown files ("climate_data_extraction_code.rmd" is the script for data extraction and cleaning and "Data_Analysis_V2.rmd" is the analysis script), the associated datasets (in .csv format), and the metadata file ("readme.txt").</p>
Reprocessing of the dataset "Plasma Proteome Profiling Reveals the Effects of Weight Loss on the Apolipoprotein Family and Systemic Inflammation Status"
<p>Reprocessing of the MassIVE repository MSV000080596, originally generated to investigate the dynamic changes in the plasma proteomes of a cohort of individuals with obesity following weight loss and maintenance. The reprocessing included all samples from 52 individuals taken right after the weight-loss process and during the weight maintenance phase of the study (Weeks 0, 4, 13, 26, 39, and 52).</p> <p>We used the sequence database generated by ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) representing all populations from the 1000 Genomes Project (doi.org/10.5281/zenodo.10149277). For the search, SearchGUI version 4.3.1 and PeptideShaker version 3.0.0 were used with the X!Tandem and Tide search engines. The modification settings specified were carbamidomethylation of C as fixed and oxidation of M, deamidation of N and Q, Pyrrolidone of E and Q, and acetylation of protein N-terminus as variable modifications. The maximum peptide length was set to 40 amino acids and the precursor and fragment ion tolerances were set to 7 and 20 ppm, respectively. Resulting PSMs were processed as described in (doi.org/10.1021/acs.jproteome.3c00243) using Percolator version 3.5 provided with features based on peptide retention time (DeepLC version 1.1.2) and fragmentation predictors (MS2PIP version 3.9.0), and filtered at a 1% estimated FDR.</p> <p>The attached file contains all the peptide-spectrum matches identified at 1% FDR. The peptides have been annotated with transcripts, genes, and alleles using the ProHap Peptide Annotator v1.1 (<a href="https://github.com/ProGenNo/ProHap_PeptideAnnotator">https://github.com/ProGenNo/ProHap_PeptideAnnotator</a>).</p>
Protein haplotype sequences obtained by ProHap from the Haplotype Reference Consortium Release 1.1 dataset
<p>Database of protein sequences obtained using ProHap (<a href="https://github.com/ProGenNo/ProHap">https://github.com/ProGenNo/ProHap</a>) on the data set of phased genotypes published by the Haplotype Reference Consortium, Release 1.1 (<a href="https://ega-archive.org/datasets/EGAD00001002729" target="_blank" rel="noopener">https://ega-archive.org/datasets/EGAD00001002729</a>). We used Ensembl v.110 for the mapping of coordinates between genes, exons, and transcripts.</p> <p>Release 1.1 of the HRC is provided aligned with the GRCh37 reference genome. We have performed a liftover to the GRCh38 reference using GeneBe (https://genebe.net/tools/liftover). Variants for which the reported alternative allele is considered as reference in GRCh38 were removed. A threshold of 1% minor allele frequency was applied to filter the remaining variants. After translation, a frequency threshold of 0.5% was applied to filter the resulting unique non-canonical sequences. The complete configuration file for the ProHap run is attached to this repository.</p> <p>This dataset contains one compressed directory, contains the following files:</p> <ul> <li>F1: The concatenated fasta file ready to be used with search engines, contains the following: <ul> <li>Protein haplotype sequences obtained by ProHap</li> <li>Reference proteome as per Ensembl v. 110</li> <li>Contaminant sequences from the cRAP project (<a href="https://www.thegpm.org/crap/">https://www.thegpm.org/crap/</a>)</li> <li>The file is provided in two formats - full and simplified. The simplified fasta contains only the artificial protein identifier and the matching gene name, and is optimised for compatibility with a wide range of tools. For annotation of peptides using the PeptideAnnotator, please provide the header (F1.2) in addition to the fasta file. </li> </ul> </li> <li>F2: Additional information about the haplotype sequences, to be used for mapping identified peptides to the original haplotypes</li> <li>F3: Translations of haplotype cDNA sequences, before merging with the reference proteome</li> </ul> <p>For further description of the files, please refer to <a href="https://github.com/ProGenNo/ProHap/wiki/Output-files">https://github.com/ProGenNo/ProHap/wiki/Output-files</a>.</p> <p>For the usage of these databases with search engines, and downstream anaylsis of identified peptides, please refer to the project's wiki page: <a href="https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches">https://github.com/ProGenNo/ProHap/wiki/Using-the-database-for-proteomic-searches</a>.</p> <p>When using these databases in your publication, please cite: Vašíček, J., Kuznetsova, K.G., Skiadopoulou, D. <em>et al.</em> ProHap enables human proteomic database generation accounting for population diversity. <em>Nat Methods</em> (2024). <a href="https://doi.org/10.1038/s41592-024-02506-0">https://doi.org/10.1038/s41592-024-02506-0</a></p>
Orbicella faveolata and O. franksi coral metagenome assemblies from the Lower Florida Keys region of Florida, USA
<div> <p>The enclosed files include mostly <em>Orbicella faveolata</em> and three <em>Orbicella franksi</em> coral metagenome assemblies collected from the Lower Keys in Florida’s Coral Reef, USA. Metadata for the files is included in this repository. Apparently healthy coral tissue cores were collected between May 28 and June 21, 2021. The DNA was extracted from the host and associated microorganisms and sequenced in a paired-end 150 bp format on an Illumina NovaSeq. Trimming and quality filtering of DNA sequence reads proceeded, followed by host and photoendosymbiotic dinoflagellate DNA removal. The host-cleaned reads were assembled individually by coral sample into longer contigs using MegaHit v1.1.4. The “Assembly_Fastas” zipped file contains 41 metagenome assemblies from the individual <em>Orbicella faveolata</em> corals and 3 assemblies from the individual <em>Orbicella franksi </em>colonies for a total of 44 assemblies. In addition, these assemblies were annotated with eggnog-mapper v2.1.6 to generate both predicted gene regions and annotation output files. The “Predicted_Gene_Fastas” zipped file contains nucleotide fasta files of the predicted gene regions for all 44 coral metagenome assemblies. The fasta header of each gene includes the contig ID it originated from in the associated “Assembly_Fasta”. The “Predicted_Gene_Annotations” zipped file contains either .csv or .xlsx files with the eggnog-mapper-based annotations. These files contain a “query contig” that corresponds to the contig ID in the fasta header of the “Predicted_Gene_Fasta”. </p> <p>In addition to individual assemblies, a co-assembly was generated that included all 41 <em>Orbicella faveolata</em> coral samples. Prior to co-assembly, further removal of eukaryotic DNA proceeded by splitting the indiviudual assemblies into eukaryotic and prokaryotic content with the program EukRep v0.6.7, followed by mapping of the host-clean reads to the eukaryotic DNA to remove them. The eukaryote-clean reads from all 41 corals were input into MegaHit to generate a co-assembly. The co-assembly is included (FLK_OFAV_MG_coassembly_final.contigs.fa). Predicted genes from the co-assembly were generated with Prodigal v2.6.3 and the nucleotide fasta of the output is included in this repository (FLK_OFAV_MG_pred.fna). Like with the indiviudal assemblies, eggnog-mapper was used to generate annotations of the predicted genes from Prodigal (FLK_OFAV_MG.emapper.annotations.xlsx). Additionally, the abundance of each predicted gene was generated using Salmon to map the eukaryote-clean reads to the predicted genes. The number of reads (counts) for each gene across each coral sample were aggregated as integers into one table and included in this repository (FLK_OFAV_MG_pred_NumReads.tsv). </p> </div> <div> <p>These data were processed and generated by Julie Meyer’s Lab at the University of Florida, using funding from the Florida Department of Environmental Protection. </p> </div>
3-D velocity field of the Tibetan Plateau due to land water loading
<h3>Basic information:</h3> <p>This dataset includes a series of 3-D loading deformation velocity fields, which are supplements to the GRL paper entitled "Present-Day Three-Dimensional Crustal Deformation Velocity of the Tibetan Plateau Due to Multi-Component Land Water Loading" [<a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a>]. The deformation velocities are fitted using long time span data during 2000-2020, and the detailed description of the data processing and calculation methods can be found through the GRL paper. There are results of three different grid resolutions (0.5x0.5, 0.25x0.25, 0.1x0.1), and for distinction, different file naming suffixes are used. For example, '0point5grids' indicates the results are in 0.5-degree grid resolutions (0.5x0.5), and so forth.</p> <h3>Application scenario:</h3> <p>The velocity fields here can be directly used for the analysis of crustal deformation or used for the separation of land water-induced loading deformation within geodetic deformation velocity fields over Tibetan Plateau. There are results of all the six main land water components, including soil moisture (SM) [Table S1], snow water equivalent (SWE) [Table S2], glacier [Table S3], lake [Table S4], permafrost (PM) [Table S5] and groundwater storage (GWS) [Table S6], thus users can choose one or some they focus on, or directly choose the sum of all the six main components (i.e., GRACE-inferred total terrestrial water storage [Table S7]).</p> <h3>Citation: </h3> <p>When using this dataset, please cite the GRL paper: Jiao, J., Pan, Y., Ren, D., & Zhang, X. (2024). Present-day three-dimensional crustal deformation velocity of the Tibetan Plateau due to multi-component land water loading. <em>Geophysical Research Letters</em>, 51, e2024GL108684. <a href="https://doi.org/10.1029/2024GL108684">https://doi.org/10.1029/2024GL108684</a></p> <h3>Contents:</h3> <p>Table S1. 3-D velocity field of the Tibetan Plateau due to the loading of soil moisture (SM).</p> <p>Table S2. 3-D velocity field of the Tibetan Plateau due to the loading of snow water equivalent (SWE).</p> <p>Table S3. 3-D velocity field of the Tibetan Plateau due to the loading of glacier.</p> <p>Table S4. 3-D velocity field of the Tibetan Plateau due to the loading of lake.</p> <p>Table S5. 3-D velocity field of the Tibetan Plateau due to the loading of permafrost (PM).</p> <p>Table S6. 3-D velocity field of the Tibetan Plateau due to the loading of groundwater storage (GWS).<br>Table S7. 3-D velocity field of the Tibetan Plateau due to the loading of GRACE-inferred total terrestrial water storage (TWS).</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2023 - May 2024
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2023 up to May 2024 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: TIME in UTC [yyyy-MM-ddThh:mm:ssZ]; Latitude [deg]; Longitude [deg]; nominal depth [m]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature [°C]; Conductivity [mmS/cm]. Missing data are defined as NaN.</p>
Holdridge Life Zones Classification in New Caledonia Habitats
<h1>Description</h1> <p>This dataset aims to represent, in geographic space, the distribution of life zones as first defined by Holdridge in 1947 and updated in 1967. Life zones are delineated through three parameters:</p> <ul> <li>Mean Annual Biotemperature (°C): This axis represents the average annual temperature, considering only temperatures above 0°C, as it influences biological activity. It determines the thermal regime of the environment.</li> <li>Annual Precipitation (mm): This axis measures the total annual precipitation, indicating moisture availability. It is crucial for determining the hydric regime and supporting different types of vegetation and ecosystems.</li> <li>Potential Evapotranspiration Ratio (PET): This axis is the ratio of potential evapotranspiration to annual precipitation. It reflects the balance between water demand and supply, indicating aridity or humidity levels and influencing vegetation types and ecosystem dynamics.</li> </ul> <p>We used a combination of WorldClim datasets (Biotemperature and potential evapotranspiration) and Météo-France Aurelhy datasets (Annual Precipitation) specifically designed for New Caledonia to produce the raster with a 1 km² resolution.</p> <h1>Content</h1> <p>This dataset was produced, analyzed, and verified using a combination of open-source software, including QGIS, PostgreSQL, PostGIS, Python, R and the GDAL library, all running on Linux.</p> <ul> <li>amap_raster_holdridge_nc.tif is a GeoTIFF, utilizing the WGS84 international coordinate system, and consists of a single band with three major classes coded as <ul> <li>Dry life zone (rast = 1)</li> <li>Moist life zone (rast = 2)</li> <li>Rain life zone (rast = 3)</li> </ul> </li> <li>holdridge_3classes_NC.png is an image illustrating the valid domain of life zones in New Caledonia and the classification used in the dataset.</li> </ul> <h1>Limitations</h1> <p>Strictly, the classification leads to five distinct classes (very dry, dry, moist, wet, and rain), but as the two extreme classes cover less than 0.5% of New Caledonia, we merged very dry and dry into the "dry" class, as well as wet and rain into the "rain" class as illustrated in the Figure <a href="../api/records/12731521/draft/files/holdridge_3classes_NC.png/content" target="_blank" rel="noopener noreferrer">holdridge_3classes_NC.png</a>.</p>
Forest Map of New Caledonia
<h1>Description</h1> <p>This dataset contains the shapefile of the Caledonian forest produced by digitization at a scale of 1:3,000 from a mosaic of satellite images (Sentinel 2, Quickbird, Pléiades) and aerial photographs provided by the department of infrastructure, topography and land transport (DITTT) of the government of New Caledonia and available on the georep map server (<a href="https://georep.nc/" target="_blank" rel="noopener">georep.nc</a>). Satellite images and aerial photographs were taken between 2009 and 2021 with a maximum spatial resolution of 0.5 m. We classified the vegetation as a forest when the plants (which must be over 5 meters tall) formed a continuous canopy, obscuring the ground surface over an area of at least 0.5 hectares (FAO 2020). Finally, we consider a polygon as an isolated forest fragment if it is more than 10 meters away from another polygon. When the distance is less than 10 meters, the polygons are merged.</p> <p>Each of the 12,058 digitized polygons was extensively cross-checked with the Tropical Rainforest Cover Change (TMF) dataset using 41 years of Landsat time series (<a href="https://www.biorxiv.org/content/10.1101/2020.09.17.295774v1" target="_blank" rel="noopener">Vancutsem et al., 2021</a>) and the ETH model providing 10 m resolution of global canopy height (<a href="https://www.nature.com/articles/s41559-023-02206-6" target="_blank" rel="noopener">Lang et al., 2023</a>). This consistency analysis between photo-interpretation and radiometric detection was conducted on a 2 km² grid, with verification of each polygon when the divergence exceeded 15%. In total, 1564 polygons, either barely discernible in the images or exhibiting a homogeneous canopy texture, were visually verified by helicopter overflight. At least, forest polygons were verified through databases of plant occurrences collected in the forest, in particular the <a title="NOU Herbarium" href="http://publish.plantnet-project.org/project/nou" target="_blank" rel="noopener">Herbarium of New Caledonia database</a> (NOU) and the <a title="NC-PIPPN" href="https://amap.cirad.fr/reseauxparcelles/npippn.php" target="_blank" rel="noopener">New Caledonian Plant Inventories and Permanent Plots Network </a>(NC-PIPPN).</p> <h1>Content</h1> <p>This dataset was generated, analyzed, and validated using a suite of open-source software tools, including QGIS, PostgreSQL, PostGIS, Python, and the GDAL library, operating on a Linux platform. The compressed file includes six essential files formatted for an ESRI GIS system, utilizing the WGS84 international coordinate system. It is compatible for upload into spatial databases such as PostgreSQL/PostGIS..</p> <p>Each entry in the attribute table represents a forest fragment (a polygon) with associated fields (restricted to 10 characters) :</p> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td><strong>id</strong></td> <td>INTEGER</td> <td>Unique identifier</td> </tr> <tr> <td><strong>pn</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Northern province</td> </tr> <tr> <td><strong>ps</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Southern province</td> </tr> <tr> <td><strong>pil</strong></td> <td>BOOLEAN</td> <td>True or False, indicating if the polygon overlaps with the Islands province (Loyalty Islands)</td> </tr> <tr> <td><strong>area_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares</td> </tr> <tr> <td><strong>dry_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the dry life zone according to Holdridge</td> </tr> <tr> <td><strong>moist_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the moist life zone according to Holdridge</td> </tr> <tr> <td><strong>rain_ha</strong></td> <td>NUMERIC (2 DECIMALS)</td> <td>Area of the polygon in hectares within the rain life zone according to Holdridge</td> </tr> <tr> <td><strong>created_by</strong></td> <td>TEXT</td> <td>Creator of the polygon</td> </tr> <tr> <td><strong>date_creat</strong></td> <td>DATE</td> <td>Date when the polygon was first created</td> </tr> <tr> <td><strong>date_updat</strong></td> <td>DATE</td> <td>Date when the polygon was updated</td> </tr> </tbody> </table> <h1>Limitations</h1> <p>Currently, only the North and South provinces are available, an area of 16,395 km² out of a total of 18,345 km². Digitization of the Loyaulties Islands will be available soon. This dataset is periodically updated with corrections made by field observations and the addition of new expert interpretations, especially on smaller islands that have not been digitized before. We plan to produce the whole map step by step.</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.