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FIGURE 6 in The end of the long road: Iranian endemic Seseli elbursense (Apiaceae) has taken its place in the genus Semenovia
FIGURE 6. Bayesian analysis phylogenetic tree of nrITS/ETS nucleotide sequences. Posterior probability values and bootstrap values of maximum parsimony analysis are shown.
FIGURE 5 in The end of the long road: Iranian endemic Seseli elbursense (Apiaceae) has taken its place in the genus Semenovia
FIGURE 5. Bayesian analysis phylogenetic tree of nrITS nucleotide sequences. Posterior probability values and bootstrap values of maximum parsimony analysis are shown.
FIGURE 4 in The end of the long road: Iranian endemic Seseli elbursense (Apiaceae) has taken its place in the genus Semenovia
FIGURE 4. Semenovia subscaposa, holotype (W 1958-0003384). Courtesy of the Naturhistorisches Museum Wien.
FIGURE 4. Vincetoxicum stylesii. A, Leaves from a long and a in A new endemic Vincetoxicum (Apocynaceae) in the Eastern Cape, and a conspectus of the genus for South Africa
FIGURE 4. Vincetoxicum stylesii. A, Leaves from a long and a short shoot; A´, Leaf from a long shoot; B, Flower in top view; C, Flower from adaxial side; D, Gynostegium with corona in lateral view; E, Style-head with two pollinaria still attached (natural position after removal of anthers); F, Pollinarium. Drawn by U. Meve from Styles 5401 (A, B) and Styles 5722 (A´, C–F).
COG and Pfam annotation results of the genome sequences of four novel Endozoicomonas strains associated with the octocoral Litophyton in a long-term aquarium facility
<p>COG and Pfam annotation files from DOE-JGI Microbial Genome Annotation Pipeline (MGAP) version 4(1), for four <em>Endozoicomonas</em> strains associated with the tropical octocoral Litophyton in a long-term aquarium facility. Data correspond to the assemblies of NE35, NE40, NE41, and NE43, available under the BioProject accession numbers <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075803">PRJNA1075803</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075804">PRJNA1075804</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075805">PRJNA1075805</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075806">PRJNA1075806</a>, respectively. Results were submitted to the Integrated Microbial Genomes and Microbiomes system v7 (IMG/M) (2) for comparative analysis. The genome annotations can be interactively accessed on IMG/M (https://img.jgi.doe.gov/cgi-bin/m/main.cgi) using the following identifiers: 8036267134 (strain NE35), 8036277142 (strain NE40), 8045494135 (strain NE41); 8036272146 (strain NE43). </p> <p>This dataset is part of the following study:</p> <p>Marques M, da Silva DMG, Santos E, Baylina N, Peixoto R, Kyrpides NC, Woyke T, Whitman WB, Keller-Costa T, Costa R. 2024. Genome sequences of four novel <em>Endozoicomonas </em>strains associated with a tropical octocoral in a long-term aquarium facility. Microbiology Resource Announcements</p> <p> </p> <p>Other reference sources:</p> <p>(1) Huntemann M, Ivanova NN, Mavromatis K, James Tripp H, Paez-Espino D, Palaniappan K, Szeto E, Pillay M, Chen IMA, Pati A, Nielsen T, Markowitz VM, Kyrpides NC. 2015. The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4). Stand Genomic Sci 10:1–6.</p> <p>(2) Chen IMA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, Hajek P, Ritter SJ, Webb C, Wu D, Varghese NJ, Reddy TBK, Mukherjee S, Ovchinnikova G, Nolan M, Seshadri R, Roux S, Visel A, Woyke T, Eloe-Fadrosh EA, Kyrpides NC, Ivanova NN. 2023. The IMG/M data management and analysis system v.7: content updates and new features. Nucleic Acids Res 51:D723–D732.</p>
No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity
<p>This is the EEG and behavioral data for the long-term memory ping study by Sander van Bree, Abbie Sarah Mackenzie, and Maria Wimber.</p> <p>Paper title: No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity</p> <p>The behavioral data is named "behav_res_pp", which is the output of script 1 (s0_extractdata.m) on Github. The EEG data is named "pp_reorder" and it is the output of script 5 (s5_correctdata.m); i.e., it is both preprocessed and correctly formatted for the main analyses.</p> <p>Move the behavioral data to folder /data/behav_data/ and the EEG data to folder /data/eeg_data/</p> <p>For the analysis scripts and more information, check Github: https://github.com/sandervanbree/MemPing</p>
Low lAtitude long Range Ionospheric raDar (LARID) and related ionospheric data
<p>The low latitude long range ionospheric radar (LARID) echo, Global Navigation Satellite System (GNSS) data, ionosonde data, and all-sky VHF radar echo data are from the Ionospheric observational network for irregularity and scintillation in East/Southeast Asia (IONISE), the Chinese Meridian Project (http://data.meridianproject.ac.cn), and are archived at the Geophysics Center, National Earth System Science Data Center at BNOSE, IGGCAS (http://wdc.geophys.ac.cn/). The LARID at Dongfang are used to investigate the ionospheric irregularities and disturbances in a wide longitude span ~90 degree or more. The LARID worked at 20.4 MHz under a beam steering mode within the azimuth -24°-24° (clockwise due boresight of the radar in the east and west directions), stepped by 3°. For each beam, the radar can receive signals from the slant ranges of 45 km to 4500 km or more, with typical temporal (range) resolutions ranging between 1 and 8 min (15 and 60 km). In addition, the ionosonde ionograms obtained at Chiang Mai, and Ledong, with temporal resolutions of 7.5 min were manually scaled to get foEs and the virtual height of different plasma frequency. The echoes from the all-sky VHF radar at Ledong are used to investigate E region irregularities. The TEC and S4 index with resolutions of 30 s and 1 min respectively are used to investigate different kinds of ionospheric irregularities and disturbances.</p>
Figure 2 in A multidisciplinary approach unveils the distribution of the Alpine long-eared bat Plecotus macrobullaris (Vespertilionidae) in Italy
Figure 2: Phylogenetic analyses of long-eared bats. (Left) origin of molecular samples analysed from Italy; (right) bootstrapped consensus ML phylogenetic tree (10,000 iterations) obtained from the analysis of COI gene fragment of Plecotus bats. Coloured sequences represent new genetic sequences and refer to different species. The statistical support of major clades is shown at their nodes (ML bootstrap support).
Figure 1 in A multidisciplinary approach unveils the distribution of the Alpine long-eared bat Plecotus macrobullaris (Vespertilionidae) in Italy
Figure 1: Summary of the distribution of the Alpine long-eared bat in Italy. Colour coding: yellow, non-genotyped (scientific and grey literature); orange, non-genotyped (new occurrences); purple, genotyped.
Figure 1 in First record of albinism in long-nosed mongoose Xenogale naso documented with camera traps in the Yoko Council Forest, Centre Cameroon
Figure 1: Map showing the camera trap stations with mongoose species in the Yoko Council Forest, Cameroon.
Figure 2 in First record of albinism in long-nosed mongoose Xenogale naso documented with camera traps in the Yoko Council Forest, Centre Cameroon
Figure 2: The recorded albino long-nosed mongooses in a grassland savannah (a) and in a swamp dominated by raffia palms (b and c) of Yoko Council Forest, Cameroon.
Figure 3 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 3: Median-joining network constructed using cyt-b sequences of Hemiechinus auritus. The size of each circle is proportional to the frequency of the haplotype in the sample. Median vectors are indicated by blank circles. Each mutation between haplotypes is represented by a bar. For the geographical origins of the sequences, see Figure 1, Table 1 and Supplementary Table S1.
Figure 2 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 2: Results of MP, ML and BI analyses combined on an ML tree based on cyt-b sequences of Hemiechinus auritus. Numbers at nodes indicate bootstrap support values (MP and ML)/posterior probabilities (BI). Bootstrap values ≥ 90 % and Bayesian posterior probabilities ≥0.90 are shown. For the geographical origins of the sequences, see Figure 1, Table 1 and Supplementary Table S1.
Figure 1 in Mitochondrial genetic variation in long-eared hedgehogs, Hemiechinus auritus, from the Anatolian Peninsula and Cyprus
Figure 1: Map showing the distribution range of the five main geographical lineages (I.–V. MGL) we discussed based on cyt-b data. It shows the match of the mitochondrial genetic structuring with named sub-species that have previously been described. Location numbers on the map for Hemiechinus auritus samples sequenced for the present study are listed in Table 1. For the geographical origins of the published sequences, see Supplementary Table S1.
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
DO UNITED STATES STOCK INDICES AND GOLD PRICES DETERMINE BITCOIN PRICES? EVIDENCE FROM NONLINEAR SHORT- AND LONG-RUN ASYMMETRIC APPROACHES
<p>Prices of Bitcoin, US stock indices, Gold and crude oil</p>
D3.10 - Users document for methods and models of the long-term coastline evolution.
<p>The D3.10, related to Task 3.5 and entitled "User Document for Methods and Models of the Long-term Coastline Evolution," is a WP3 deliverable, specifically a report describing the methods and models used to study long-term morpho-dynamic processes in a climate-change scenario. </p>
Long-term Changes in Salinity in the South China Sea due to Anthropogenic Forcing
Open the record for dataset details and reuse information.
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Density-Based Long-Range Electrostatic Descriptors - Datasets
<p>Datasets in the VASP ML_AB format.</p> <p>ML_AB_NaCl_liqiud: 64 Na atoms, 64 Cl atoms, 1014 configurations<br>ML_AB_NaCl_solid: 64 Na atoms, 64 Cl atoms, 1151 configurations<br>ML_AB_point_charges: 32 +1 charged atoms, 32 -1 charged atoms, 2000 configurations<br>ML_AB_ZrO2: 32 Zr atoms, 64 O atoms, 592 configurations</p> <p>For more details, see preprint: <br>https://arxiv.org/abs/2406.17595</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.