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A standards mapping for bio-products
<p><a href="https://www.uni.com/en/">UNI, Italian standards body</a>, has realized this first version of a mappig of around 150 standards at International (ISO/IEC/ITU for example), European (CEN/ETSI), and national (UNI) level related to the topics relevant within Biorecer project. </p> <p>The mapping includes the reference number of the document, its title, where available the scope and the technical committee developing each document. </p> <p>This mapping will be implemented and paired with a Open Standards Research Tool, which will be freely available on <a href="https://biorecer.eu/" target="_blank" rel="noopener">Biorecer website</a> (ideally by September 2024). </p> <p>Moreover, the methodology behind the mapping, an extension of the analysis will be detailed in a dedicated public project deliverable. </p> <p><strong>Disclaimer</strong>: Data as of May 2024. </p>
Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards
<h1>Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards</h1> <p>In this community effort, we compared measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as new suite of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method (SOP) and/or their method of choice (OTHER). Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively.</p> <p>The attached file "ILS-Ceramide-Ring-Trial-Datasets.csv" and the table below map the lab number (LabNum) used in the manuscript in all plots to the originally assigned submission Id (LabId) used in <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial/tree/main/data/original-reports" target="_blank" rel="noopener">the anonymized reports </a>containing the peak areas submitted by each lab for their SOP (Standard) and / or OTHER (Preferred) workflow. The table below provides further information on the separation used (LC), the mass analyzer type (QQQ=Triple Quads and Traps, Orbitrap, TOF) and the associated mass analyzer resolution (LowRes, HighRes), and links each LabNum to the corresponding dataset name and Zenodo DOI, if available. In order to retain the anonymity of all participating labs w.r.t. the submitted datasets, only the converted mzML files are provided in the linked submissions. Please note that labs were free to choose whether they wanted to disclose their MS data or not. Thus, missing datasets indicate that the corresponding lab did not provide their raw / mzML data. </p> <p>All reports together with the code for analysis and visualization, reproducing the figures in the manuscript, are available under the following doi: <a href="../doi/10.5281/zenodo.10081970" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.10081970</a>. This links to releases of the following GitHub repository: <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial">https://github.com/lifs-tools/ils-ceramide-ring-trial</a>. </p> <h2>Ring Trial mzML Datasets</h2> <table> <tbody> <tr> <th>LabNum</th> <th>LabId</th> <th>Protocol</th> <th>LC</th> <th>MassAnalyzerType</th> <th>MassAnalyzerResolution</th> <th>DatasetName</th> <th>DOI</th> </tr> <tr> <td>1</td> <td>02b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_01_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134264" target="_blank" rel="noopener">10.5281/zenodo.13134264</a></td> </tr> <tr> <td>2</td> <td>3</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_02_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13145059" target="_blank" rel="noopener">10.5281/zenodo.13145059</a></td> </tr> <tr> <td>3</td> <td>4</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_03_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134437" target="_blank" rel="noopener">10.5281/zenodo.13134437</a></td> </tr> <tr> <td>4</td> <td>5</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_04_OTHER</td> <td> </td> </tr> <tr> <td>5</td> <td>7</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_05_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134439" target="_blank" rel="noopener">10.5281/zenodo.13134439</a></td> </tr> <tr> <td>6</td> <td>9</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_06_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134441" target="_blank" rel="noopener">10.5281/zenodo.13134441</a></td> </tr> <tr> <td>7</td> <td>10a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134445" target="_blank" rel="noopener">10.5281/zenodo.13134445</a></td> </tr> <tr> <td>7</td> <td>10b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_07_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134443" target="_blank" rel="noopener">10.5281/zenodo.13134443</a></td> </tr> <tr> <td>8</td> <td>12</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_08_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134447" target="_blank" rel="noopener">10.5281/zenodo.13134447</a></td> </tr> <tr> <td>9</td> <td>13</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_09_OTHER</td> <td> </td> </tr> <tr> <td>10</td> <td>14</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_10_SOP</td> <td> </td> </tr> <tr> <td>11</td> <td>15</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_11_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13134449" target="_blank" rel="noopener">10.5281/zenodo.13134449</a></td> </tr> <tr> <td>12</td> <td>16</td> <td>OTHER</td> <td>RP</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_12_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13134451" target="_blank" rel="noopener">10.5281/zenodo.13134451</a></td> </tr> <tr> <td>13</td> <td>17a</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_13_OTHER</td> <td> </td> </tr> <tr> <td>14</td> <td>18a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>14</td> <td>18b</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_14_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135617" target="_blank" rel="noopener">10.5281/zenodo.13135617</a></td> </tr> <tr> <td>15</td> <td>19</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_15_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135625" target="_blank" rel="noopener">10.5281/zenodo.13135625</a></td> </tr> <tr> <td>16</td> <td>20a</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_SOP</td> <td> </td> </tr> <tr> <td>16</td> <td>20b</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_16_OTHER</td> <td> </td> </tr> <tr> <td>17</td> <td>21</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_17_SOP</td> <td> </td> </tr> <tr> <td>18</td> <td>22</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_18_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135627" target="_blank" rel="noopener">10.5281/zenodo.13135627</a></td> </tr> <tr> <td>19</td> <td>23</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_19_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135629" target="_blank" rel="noopener">10.5281/zenodo.13135629</a></td> </tr> <tr> <td>20</td> <td>24</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_20_SOP</td> <td> </td> </tr> <tr> <td>21</td> <td>25</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_21_SOP</td> <td><a href="../doi/10.5281/zenodo.13244007" target="_blank" rel="noopener">10.5281/zenodo.13244007</a></td> </tr> <tr> <td>22</td> <td>26a</td> <td>OTHER</td> <td>FIA</td> <td>Orbitrap</td> <td>HighRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>22</td> <td>26b</td> <td>OTHER</td> <td>FIA</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_22_OTHER</td> <td> </td> </tr> <tr> <td>23</td> <td>27</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_23_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135632" target="_blank" rel="noopener">10.5281/zenodo.13135632</a></td> </tr> <tr> <td>24</td> <td>28</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_24_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13135634" target="_blank" rel="noopener">10.5281/zenodo.13135634</a></td> </tr> <tr> <td>25</td> <td>29a</td> <td>SOP</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_25_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136167" target="_blank" rel="noopener">10.5281/zenodo.13136167</a></td> </tr> <tr> <td>25</td> <td>29b</td> <td>OTHER</td> <td>SFC</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_25_OTHER</td> <td><a href="https://doi.org/10.5281/zenodo.13135638" target="_blank" rel="noopener">10.5281/zenodo.13135638</a></td> </tr> <tr> <td>26</td> <td>30</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_26_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136171" target="_blank" rel="noopener">10.5281/zenodo.13136171</a></td> </tr> <tr> <td>27</td> <td>31</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_27_SOP</td> <td><a href="https://doi.org/10.5281/zenodo.13136176" target="_blank" rel="noopener">10.5281/zenodo.13136176</a></td> </tr> <tr> <td>28</td> <td>32</td> <td>OTHER</td> <td>RP</td> <td>TOF</td> <td>HighRes</td> <td>Lab_28_OTHER</td> <td> </td> </tr> <tr> <td>29</td> <td>33</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_29_OTHER</td> <td> </td> </tr> <tr> <td>30</td> <td>34</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_30_SOP</td> <td> </td> </tr> <tr> <td>31</td> <td>35</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_31_OTHER</td> <td> </td> </tr> <tr> <td>32</td> <td>36</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_32_SOP</td> <td><a href="../doi/10.5281/zenodo.13166454" target="_blank" rel="noopener">10.5281/zenodo.13166454</a></td> </tr> <tr> <td>33</td> <td>37</td> <td>OTHER</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_33_OTHER</td> <td><a href="../doi/10.5281/zenodo.13732469" target="_blank" rel="noopener">10.5281/zenodo.13732469</a></td> </tr> <tr> <td>34</td> <td>38</td> <td>SOP</td> <td>RP</td> <td>QQQ</td> <td>LowRes</td> <td>Lab_34_SOP</td> <td><a href="../doi/10.5281/zenodo.13324807" target="_blank" rel="noopener">10.5281/zenodo.13324807</a></td> </tr> </tbody> </table>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Gold standard corpus, ontologies, and Entity-Quality ontology annotations for evolutionary phenotypes
<p>This data set includes a gold-standard corpus of evolutionary phenotype descriptions (in the form of character state descriptions pulled from a variety of phylogenetic systematics studies), and their corresponding expert-curated annotations with ontology terms in the form of Entity-Quality (EQ) statements. EQ annotatons allow machine-reasoning (through the semantics encoded in the requisite ontologies from which the ontology terms are drawn), and machine-reasoning in turn enables computing metrics for quantifying the semantic similarity between different phenotype descriptions as represented by their EQ annotations.</p> <p>Also included are the ontologies, and the human expert-generated and Semantic Charaparser (i.e., machine) generated EQ annotations used to assess Semantic Charaparser performance relative to inter-curator variation and to the effect of having access to external knowledge. The ontologies include those used as input, the "augmented" ontologies created by human curators in each experiment round, and the merged ontology used to maximize Semantic Charaparser's performance.</p> <p>The production of the gold standard corpus, annotation experiments, and evaluation of the results are described in detail in the following manuscript:</p> <blockquote> <p>Dahdul et al (2018) Annotation of phenotypes using ontologies: a Gold Standard for the training and evaluation of natural language processing systems. BioRxiv https://doi.org/10.1101/322156. Submitted to Database.</p> </blockquote> <p>The analysis code for evaluating the gold standard corpus (and the input data and ontologies for that) are available separately from the following:</p> <blockquote> <p>Manda et al (2018) Code and data for analysis of evolutionary phenotype ontology annotations and gold standard corpus. Zenodo. https://doi.org/10.5281/zenodo.1218010</p> </blockquote> <p>In comparison to the previous version (v1.0.0), this record includes a file of MD5 checksums of the Gold Standard data files. The data files themselves are unchanged.</p>
Group Contribution Models for Standard molar enthalpy of formation of gas phase at 298.15K
<p>A data set of several models for Heat of formation based on additive schemes (group contribution models).</p> <p>Model building is performed by means of Ambit-GCM software (<a href="http://ambit.sourceforge.net/">http://ambit.sourceforge.net/</a>, <a href="https://doi.org/10.5281/zenodo.1470793">https://doi.org/10.5281/zenodo.1470793</a>).</p> <p>QSPR models were built with a small training data set mainly for demonstration of Ambit-GCM software usage.</p>
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p> </p> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>silt.wfraction = variable: silt weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Soil available water capacity in mm derived for 5 standard layers (0-10, 10-30, 30-60, 60-100 and 100-200 cm) at 250 m resolution
<p>Available Water Capacity (in mm) derived by calculating Water Retention Difference (difference between the field capacity and wilting point; see <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054247">NRCS Soil Survey Laboratory Methods Manual</a>), and then summing up WRD for all standard layers (0–200 cm). Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution is available <a href="https://doi.org/10.5281/zenodo.2609113"><strong>here</strong></a>. These estimates ignore depth to bedrock i.e. existence of any impenetrable layer (total available capacity over the whole land mass is likely about 10–15% smaller). Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a></li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>available.water.capacity = available water capacity in mm,</li> <li>usda.mm = determination method: Water Retention Difference in mm,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..10cm = vertical reference: 0-10 cm layer below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
UPS2 standard in solution converted to mzML with entrapment and decoy appended protein databases
<p>RAW data downloaded from: <a href="http://data.marcottelab.org/MSdata/Data_13/DATA/Marcotte_0909/">http://data.marcottelab.org/MSdata/Data_13/</a></p> <p>Converted to mzML with ThermoRawFileConverter.</p> <p>It is one of the datasets used in the EPIFANY publication.</p>
Validation of Emission Spectroscopy Gas Temperature Measurements Using a Standard Flame Traceable to the International Temperature Scale of 1990 (ITS-90)
<p>Data underpinning the associated publication (https://doi.org/10.1007/s10765-019-2557-6) on accurate traceable measurement of post-flame temperatures.</p>
CLDF dataset on Panoan Languages in Standardized Transcription derived from Key and Comrie's "Intercontinental Dictionary Series" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Miller, J. and List, J.-M. (2024): Providing standardized phonetic transcriptions for the Panoan languages in the Intercontinental Dictionary Series. Computer-Assisted Language Comparison in Practice 7.2. URL: https://calc.hypotheses.org/7503.</p> </blockquote>
Supply chain sustainability standards in temperate farming commodities
<p>Could companies contribute to farmland biodiversity in temperate landscapes? Most of the business efforts to enhance sustainability in agricultural supply chains have been focused on tropics. Important temperate crops like cereals and oilseeds are left largely unchecked. This results in a missed opportunity to reverse biodiversity decline in about 47% of the world's cropland. But implementation of voluntary sustainability standards to restore biodiversity in temperate and Mediterranean biomes would require doing things differently than in tropical commodities. Temperate agriculture differs both in its operations (lack of divergence between staples and cash crops, crop rotation) and landscape context (lack of agricultural expansion frontiers, complex pressures on biodiversity). Consequently, agricultural certification and similar schemes need to be linked primarily to a unit of space rather than a specific commodity. However, necessary tools such as advanced criteria, metrics and data infrastructure are lacking in temperate agriculture, and their development depends on input from conservation scientists and practitioners.</p> <p>Supporting information for<em> Supply chain sustainability standards in temperate farming commodities </em>consists of:</p> <ol> <li>Dataset for Figure 1;</li> <li>Additional information on methodology (VSSs uptake in retail industry) and sources (LEAF Marque case study, references for evidence of impact of feasible objectives for VSSs in conventional temperate agriculture).</li> </ol> <p> </p>
MADERA: A standardized Pan-Amazonian dataset for tropical timber species
<p>We compiled and presented a dataset for all timber species reported in the Amazon region from all nine South American Amazonian countries. This was based on official information from every country, as well as from two substantial scientific references. We verified the standard taxonomic names from each individual source, using the Taxonomic Name Resolution Service (TNRS) and considered all Amazonian tree species with DBH ≥ 10 cm. We also obtained estimates of the current population size for most species from a published approach based on data from 1,900 tree inventory plots (1-hectare each) distributed across the Amazon region and part from the Amazon Tree Diversity Network (ATDN). We then identified the hyperdominant timber species. In addition, we overlapped our timber species list with data for species that are used for commercial purposes, according to the International Tropical Timber Organization (ITTO), the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the International Union for Conservation of Nature (IUCN) taxa assessment and Red List categories. Finally, we also included IUCN Red List categories based on combined deforestation, and climate change scenarios for these species. Our final Amazonian timber species dataset contains 1,112 unique species records, which belong to 337 genera and 72 families from the lowland Amazonian rainforest, with associated information related to population, conservation, and trade status of each species. The authors of this research expect that the information provided will be useful to strengthen the public forestry policies of the Amazon countries, inform ecological studies, as well for forest management purposes. </p>
Dataset - A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks
<p>The present dataset is open-sourced with the paper "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks".</p> <p>The paper is authored by Giuseppe Caso, Mohammad Rajiullah, Anna Brunstrom, Luca De Nardis, Ozgu Alay, and Marco Neri. </p> <p>If you use the dataset for your own research activities and publications, please consider citing the paper as follows: </p> <p><strong>G. Caso et al., "A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks," in Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24), pp. 1-7, 2024.</strong></p> <p><strong>@inproceedings{caso2024standard,</strong><br><strong> title={{A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G Networks}},</strong><br><strong> author={Caso, Giuseppe and Rajiullah, Mohammad and Brunstrom, Anna, and De Nardis, Luca and Alay, Ozgu and Neri, Marco},</strong><br><strong> booktitle={Proceedings of the IEEE Conference on Standards for Communications and Networking (IEEE CSCN'24)},</strong><br><strong> pages={1--7},</strong><br><strong> year={2024}</strong><br><strong>}</strong></p> <p>A detailed description of the dataset is provided in the paper, and the README file provides additional details.</p> <p>Contact Giuseppe Caso (giuseppe.caso@kau.se) for more information on the dataset and potential access to additional data. </p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA standard-resolution model
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Mapping Across OpenAIRE And Argos Data Models And The DMP Common Standard
<p>This is the outcome of the mapping activity across the data models of Argos and the OpenAIRE Research Graph and the RDA DMP Common Standard.</p>
A Nu Supersymmetric Anomaly-free Atlas: anomaly-free, flavour-dependent U(1) charge assignments for the Minimally Supersymmetric Standard Model plus three Standard Model-singlet superfields
<p>We present lists of anomaly-free charge assignments up to a maximum magnitude charge Qmax=10 for the chiral fermionic content of the MSSM plus 3 right-handed neutrinos. </p> <p>Due to the large number of solutions, we compress the list into the file MSSMnuRcharges_Qmax10.gz. Please note that the unzipped file is approximately 130GB in size. We additionally include a smaller file, MSSMnuRcharges_Qmax4, containing the subset of anomaly-free charge assignments up to a maximum magnitude charge Qmax=4.</p> <p>The files searchU1MSSM.cpp and searchU1MSSM.h contain C++ files (in the 2014 standard) to produce the solutions. runsearch.sh is a bash script that compiles the programs and then runs it for a sample set of inputs.</p> <p>We provide Mathematica notebooks Analytic_solution_generator.nb and Analytic_Checks.nb which respectively provide the parametrisation of the analytic solution and checks thereof.</p> <p>The files beginning 'filter' contain example programs that read in each line in the solution list, apply a filter and print only the solutions satisfying the conditions of that filter. runfilter.sh is a bash script that compiles the filters and then runs a single filter as an example.</p> <p>These data and programs are based on this paper: https://arxiv.org/abs/2107.07926.</p>
Improving Methods to Measure Comparable Mortality by Cause - Gold Standard Verbal Autopsy Data 2011-2014
<p>These data were collected and compiled as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) project, funded by Australia's National Health and Medical Research Council (NHMRC). Verbal autopsies (VAs) were conducted between 2011 and 2014 in three sites: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Diagnostic criteria and cause lists similar to those employed in the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. This study added 3512 deaths (2491 adults, 320 children, and 701 neonates) to the GS VA database created from the PHMRC study. This dataset contains the combined PHMRC and IMMCMC data for an updated GS VA database.</p>
Translation Alignment: Ancient Greek to English. Annotation Style Guide and Gold Standard.
<p>This dataset contains guidelines and a gold standard for the alignment of Ancient Greek texts with English translations.</p> <p>The guidelines were used to annotate a diverse dataset including Homeric epic, Attic prose, and Platonic dialogue, and were tested by measuring inter-annotator agreement of 80% or higher. The Ancient Greek texts used are almost entirely available through the Scaife viewer (<a href="https://scaife.perseus.org/">https://scaife.perseus.org/</a>).</p> <p>The datasets used to develop the gold standard were aligned using the Ugarit Translation Alignment Editor for Historical languages (<a href="http://ugarit.ialigner.com/">http://ugarit.ialigner.com/</a>).</p> <p>The materials available here can be used to perform and evaluate alignments of various texts in Ancient Greek, to create new gold standard corpora, and to train automated translation models.</p> <p>The guidelines can also be further adapted to address similar language pairs including an inflected and a synthetic language, such as Latin and English, or can provide a structure for the alignment of other historical texts against modern translations. However, the guidelines are not project-specific: they were specifically intended for the creation of a Gold Standard in the scenario of machine translation. Different scenarios, such as language research or pedagogy, may need further tweaking to these guidelines to make them more compatible with different underlying principles.</p> <p>For further information on Ugarit and translation alignment of historical languages, see <a href="http://ugarit.ialigner.com/bib.php">http://ugarit.ialigner.com/bib.php</a> and follow us on Twitter (@ugarit_ty).</p>
Indonesia, monthly Standardized Precipitation-Evapotranspiration Index (SPEI) blend 1960 - 2021
<p>IDN_CLI_SPEI_blend_0p042_1961_2021 is currently the only comprehensive high resolution Indonesia gridded historical dataset of SPEI blend and available for public.</p> <p>The SPEI - https://spei.csic.es/ is an extension of the widely used SPI. The SPEI is designed to take into account both precipitation and potential evapotranspiration (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.</p> <p>The IDN_CLI_SPEI_blend_0p042_1960_2021 is derived using precipitation and potential evapotranspiration from TerraClimate data - https://www.climatologylab.org/terraclimate.html, it has 0.042 degree gridded resolution, a monthly and available from 1958 to 2021. The calibration period is January 1961 to December 2020. The starting date of the dataset is 1960 in order to provide common information across the different SPEI time-scales.</p> <p>The SPEI blend integrate several SPEI scales into a single product, combine 3-, 6-, 9-, 12- and 24-month SPEI to estimate the overall dry/wet condition. </p> <p>The SPEI processed using climate_indices, an open source Python library providing reference implementations of commonly used climate indices. https://pypi.org/project/climate-indices/</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.