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Fig. 24. Prosymna ambigua, 20 in When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation
Fig. 24. Prosymna ambigua, 20 km W Lola, Namibe.
Fig. 1 in When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation
Fig. 1. Afrotyphlops mucrosa, Lungue Bungue River, Moxico Province (Photo: Werner Conradie).
Fig. 3 in When roads appear jaguars decline: Increased access to an Amazonian wilderness area reduces potential for jaguar conservation
Fig. 3. Python sebae, Cabesa da Cobra, Soyo (Photo: Warren Klein).
Accessibility Reviews Associated with Visual Disabilities or Eye Conditions
<p>A dataset of nearly 180 million user reviews from which we extracted 4,999 accessibility reviews that express concerns that affect users with visual disabilities or eye conditions. </p> <p>This data is related to the following paper:</p> <p>Alberto Dumont Alves Oliveira, Paulo Sérgio Henrique Dos Santos, Wilson Estécio Marcílio Júnior, Wajdi M Aljedaani, Danilo Medeiros Eler, and Marcelo Medeiros Eler. 2023. Analyzing Accessibility Reviews Associated with Visual Disabilities or Eye Conditions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 37, 1–14. <a href="https://doi.org/10.1145/3544548.3581315" rel="nofollow">https://doi.org/10.1145/3544548.3581315</a></p>
MIGNEX Survey metadata (restricted-access variant)
<p>This is a metadata record representing the restricted-access variant of the MIGNEX survey dataset. The data file is stored on the project's internal file hosting service, as "...\\MIGNEX-data\WP03-data\MIGNEX-Survey-dataset\mxs-restricted-v1.dta". It cannot be uploaded to Zenodo because it contains potentially identifying information.</p>
Assembled "juvenile head region" transcriptome from the hagfish Eptatretus burgeri (NCBI GenBank Accession: SRX2541845)
<p>1) The raw reads were dwonloaded from NCBI GenBank (SRA run accession: (SRR5234495) with sratoolkit.</p> <p>2) The assembly was performed with Trinity with the folllowing parameters:</p> <p>Trinity --seqType fq --max_memory 100G --left eb-hf-head-region-SRR5234495/eb-hf-head-region-SRR5234495_1.fastq --right eb-hf-head-region-SRR5234495/eb-hf-head-region-SRR5234495_1.fastq --CPU 10 --output trinity-transcriptome-eb/</p> <p>3) ORFs were predeicted with TransDecoder with the following settings:</p> <p>TransDecoder.LongOrfs -t transcriptome-eb-hf-head-region.fasta</p> <p>TransDecoder.Predict -t transcriptome-eb-hf-head-region.fasta</p> <p> </p>
Fig. 5 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 5. UPGMA of Paspalum urvillei accessions from USDA germplasm.
Fig. 4 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 4. Principal Coordinate Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 3 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 3. Principal Component Analysis of Paspalum urvillei accessions from USDA germplasm.
Fig. 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 2. The phenotypic structure of Paspalum urvillei accessions from USDA germplasm.
Fig. 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 1. Spatial clustering of Paspalum urvillei accessions from USDA germplasm.
Fig. 6 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 6. Violin plot of Paspalum urvillei accessions from USDA germplasm.
Table 2 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 2.</b> Summary statistics of evaluated variables of <i>Paspalum urvillei</i> from USDA germplasm accessions.</p><table><tbody><tr><th>Variable</th><th>Main category frequency</th><th>Trait diversity</th><th>PIC</th></tr></tbody><tbody><tr><th>Plant width</th><td>0.439</td><td>0.659</td><td>0.595</td></tr><tr><th>Foliage height</th><td>0.585</td><td>0.603</td><td>0.565</td></tr><tr><th>Plant height</th><td>0.537</td><td>0.627</td><td>0.576</td></tr><tr><th>Leaf length</th><td>0.317</td><td>0.736</td><td>0.688</td></tr><tr><th>Leaf width</th><td>0.561</td><td>0.616</td><td>0.571</td></tr><tr><th>Maturity</th><td>0.537</td><td>0.664</td><td>0.635</td></tr><tr><th>Foliage distribution</th><td>0.854</td><td>0.256</td><td>0.233</td></tr><tr><th>Stem size</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Tillers</th><td>0.951</td><td>0.093</td><td>0.089</td></tr><tr><th>Foliage amount</th><td>0.585</td><td>0.485</td><td>0.368</td></tr><tr><th>Seed production</th><td>0.805</td><td>0.330</td><td>0.300</td></tr><tr><th>Winter survival</th><td>0.268</td><td>0.829</td><td>0.809</td></tr><tr><th>Mean</th><td>0.616</td><td>0.499</td><td>0.460</td></tr></tbody></table>
Table 1 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
<p><b>Table 1.</b> Description of <i>Paspalum urvillei</i> accessions of USDA germplasm.</p><table><tbody><tr><th>Plant ID</th><th>Plant #</th><th>Taxonomy</th><th>Country</th><th>City</th></tr></tbody><tbody><tr><th>PI 164065</th><td>1</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Florianópolis</td></tr><tr><th>PI 202046</th><td>2</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 203747</th><td>3</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>São Gabriel</td></tr><tr><th>PI 203749</th><td>4</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 203752</th><td>5</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Porto Alegre</td></tr><tr><th>PI 204237</th><td>6</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Rivera</td></tr><tr><th>PI 276255</th><td>7</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Montevideo</td></tr><tr><th>PI 283025</th><td>8</td><td><i>P. urvillei</i></td><td>Uruguay (3),</td><td>Tacuarembo</td></tr><tr><th>PI 304040</th><td>9</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304041</th><td>10</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304042</th><td>11</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304043</th><td>12</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304044</th><td>13</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304045</th><td>14</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Montenegro</td></tr><tr><th>PI 304047</th><td>15</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304048</th><td>16</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 304050</th><td>17</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310253</th><td>18</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 310255</th><td>19</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Tupancireta</td></tr><tr><th>PI 310257</th><td>20</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310258</th><td>21</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310263</th><td>22</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Pelotas</td></tr><tr><th>PI 310266</th><td>23</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Julio de Castilho</td></tr><tr><th>PI 364984</th><td>24</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>Limpopo</td></tr><tr><th>PI 365131</th><td>25</td><td><i>P. urvillei</i></td><td>Swaziland (6)</td><td>Malkerns</td></tr><tr><th>PI 202296</th><td>26</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 300079</th><td>27</td><td><i>P. urvillei</i></td><td>South Africa (4)</td><td>East London</td></tr><tr><th>PI 303957</th><td>28</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Uruguaiana</td></tr><tr><th>PI 304146</th><td>29</td><td><i>P. urvillei</i></td><td>Mexico (5)</td><td>Lagos-Aquascalientes</td></tr><tr><th>PI 404506</th><td>30</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Vacaria</td></tr><tr><th>PI 404885</th><td>31</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Paysandu</td></tr><tr><th>PI 404886</th><td>32</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Tranqueras</td></tr><tr><th>PI 462304</th><td>33</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462305</th><td>34</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Cerro Largo</td></tr><tr><th>PI 462306</th><td>35</td><td><i>P. urvillei</i></td><td>Uruguay (3)</td><td>Salto</td></tr><tr><th>PI 509008</th><td>36</td><td><i>P. urvillei</i></td><td>Brazil (1)</td><td>Ivoti</td></tr><tr><th>PI 509013</th><td>37</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Villa Maria</td></tr><tr><th>PI 509014</th><td>38</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Olivia</td></tr><tr><th>PI 509015</th><td>39</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Arroyito</td></tr><tr><th>PI 509016</th><td>40</td><td><i>P. urvillei</i></td><td>Argentina (2)</td><td>Buenos Aires</td></tr><tr><th>PI 509017</th><td>41</td><td><i>P. urvillei</i></td><td>Bolivia (7)</td><td>Villa Montes</td></tr></tbody></table>
Re-analysis of chromatin accessibility QTLs from the Kumasaka et al, 2018 study
<p>ATAC-seq data from <a href="https://doi.org/10.1038/s41588-018-0278-6">Kumasaka et al, 2018</a> was processed with the <a href="https://github.com/nf-core/atacseq/tree/2.1.2">nf-core/atacseq</a> v2.1.2 pipeline using Nextflow v23.09.3. We aligned raw ATAC-seq reads to the GRCh38 reference genome (Homo_sapiens.GRCh38.dna.primary_assembly.fa downloaded from Ensembl) with BWA v0.7.17. We called broad peaks with MACS2 v2.2.7.1 and defined consensus peaks as the union of all peaks that were present in at least 5% of the samples. We then quantified read overlaps with the set of consensus peaks with featureCounts v2.0.1. Finally, we normalised the read counts (counts per million) and then used the inverse normal transformation to standardise the data distribution.</p> <p>Genotype data for the 91 overlapping samples were downloaded from 1000 Genomes 30x on GRCh38 <a href="https://www.internationalgenome.org/data-portal/data-collection/30x-grch38">website</a>. Finally, we used the <a href="https://github.com/eQTL-Catalogue/qtlmap">eQTL-Catalogue/qtlmap</a> v24.01.1 workflow to perform chromatin accessibility QTL analysis. We set cis window size to 200,000 bp and excluded peaks that had less than 25 variants within that window. More details of the association testing workflow can be found <a href="https://doi.org/10.1371/journal.pgen.1010932">here</a>.</p>
Genotypes of 180 soybean accessions
<p><span>The raw genotype file contains 180 soybean accessions and 52,041 SNPs in HapMap format. Genotyping was performed using the SoySNP50K Illumina Infinium BeadChip. The genotype data were used for a GWAS analysis to identify loci associated with soybean flowering and maturity. The results are presented in the article, </span><em><span>"Genome-Wide Association Study Revealed Some New Candidate Genes Associated with Flowering and Maturity Time of Soybean in Central and West Siberian Regions of Russia,"</span></em><span> published in the journal </span><em><span>Frontiers in Plant Science</span></em><span>. In the genotype file, the accessions are labeled with numbers, and Supplementary Table 1 in the article provides the correspondence between these numbers and the common names of the accessions.</span></p>
Accessing Homo- and Heteroleptic α-Diimin Complexes (M = Fe, Co)
<p>The follwoing repository contains the cartesian coordinates of the calculated structures and the energies obtained by DFT-calculations. Please cite the original source/paper.</p>
ACCESS-AM2 campaign data
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data used for the study described in Fiddes et al. (2024) '<em>The ACCESS-AM2 climate model strongly underestimates aerosol concentration in the Southern Ocean, but improving it could be problematic for the modelled climate system</em>' submitted to Atmospheric Chemistry and Physics </p> <p>Included, for each experimental simulation, are the modelled fields at either the specific campaign location or along the ship track. </p> <p>The experiments include: </p> <ul> <li>Control - dd657</li> <li>Control* - bx400</li> <li>BL NPF - cg283</li> <li>OM2 - ch543 </li> <li>PMO - cq687</li> <li>H22 - cq686</li> <li>PMO+H22 - dd153</li> <li>SSA Gust - dd154</li> </ul> <p>The campaigns include:</p> <ul> <li>CAPRICORN1</li> <li>CAPRICORN2</li> <li>MARCUS</li> <li>CAMMPCAN</li> <li>Ice2Equator</li> <li>Cold Water Trial</li> <li>PCAN</li> <li>Kennaook-Cape Grim</li> <li>Macquarie Island</li> <li>Syowa</li> </ul> <p>The code that processed the attached model data and the subsequent analysis can be found at: https://github.com/sfiddes/ACCESS_aerosol_eval</p> <p> </p>
MIGNEX Survey Dataset (open-access variant)
<p>This is the anonymized variant of the MIGNEX Survey dataset. Please see MIGNEX Handbook Chapter 10 on how the anonymization process has affected the data.</p>
Primary data for Manuscript provisionally titled Stereoselective access to bioactive cyclopropanes (+)-PPCC and (1R,2S)-2-aminomethyl-1-arylcyclopropane-1-carboxamides from (−)-levoglucosenone
<p><span>Contains HRMS and FID data for compounds described in the manuscript titled, "<span>Stereoselective access to bioactive cyclopropanes (+)-PPCC and (1<em><span>R</span></em>,2<em><span>S</span></em>)-2-aminomethyl-1-arylcyclopropane-1-carboxamides from (−)-levoglucosenone"</span></span></p> <p><span>FIDs can be opened using SpinWorks or Topspin programs.</span></p> <p> </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.