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FIG. 12 in An annotated checklist of the birds of Burkina Faso
FIG. 12. — Campethera nivosa maxima Taylor,1970, juvenile, 17.III.2011; Comoé- Léraba classified forest, Cascade Region (photo M. Pavia).
FIG. 18 in An annotated checklist of the birds of Burkina Faso
FIG. 18. — Lanius gubernator Hartlaub, 1882, Comoé-Léraba classified forest, Cascade Region, 20.III.2011(photo M. Pavia).
FIG. 14 in An annotated checklist of the birds of Burkina Faso
FIG. 14. – Falco biarmicus abyssinicus Neumann, 1904, adult male, Banfora, 30.XI.2012 (photo M. Pavia).
FIG. 26 in An annotated checklist of the birds of Burkina Faso
FIG. 26. — Cinnyris coccinigastrus (Latham, 1801), Comoé-Léraba classified forest, Cascade Region, 20.III.2011 (photo M. Pavia).
FIG. 27 in An annotated checklist of the birds of Burkina Faso
FIG. 27. — Lagonosticta rara forbesi Neumann, 1908, Folonzo, Cascade Region, 6.II.2010 (photo G. Boano).
FIG. 25 in An annotated checklist of the birds of Burkina Faso
FIG. 25. — Fraseria plumbea plumbea (Hartlaub, 1858), Comoé-Léraba classified forest, Cascade Region, 18.III.2011 (Photo M. Pavia), 9.III.2011 (photo M. Pavia).
FIG. 21 in An annotated checklist of the birds of Burkina Faso
FIG. 21. — Locustella naevia (Boddaert, 1783), Lemoroudugou Lake, Banfora, Cascade Region, 13.II.2010 (photo G. Boano).
FIG. 2 in An annotated checklist of the birds of Burkina Faso
FIG. 2. — Parks, Wildlife reserves and Protected forests of Burkina Faso (Tamini 2020, reviewed by Belemsobgo 2020).
FIG. 24 in An annotated checklist of the birds of Burkina Faso
FIG. 24. — Hirundo aethiopica aethiopica Blanford, 1869, 10.II.2010, Folonzo, Cascade Region (photo G. Boano).
FIG. 20 in An annotated checklist of the birds of Burkina Faso
FIG. 20. — Cisticola guinea Lynes, 1930, Folonzo, Comoé-Léraba classified forest, Cascade Region, 11.XII.2012 (photo M. Pavia).
Transposable element annotation Rhynchosporium commune isolate UK7
<p>To obtain a consensus sequence for each TE family, RepeatModeler v. open-4.0.7 (http://www.repeatmasker.org/RepeatModeler/) was run on the <em>R. commune</em> UK7 reference genome. The classification was based on the GIRI Repbase (v. 2018) using RepeatMasker v. open-4.0.7. (Smit, Hubley, and P. 2015; Bao, Kojima, and Kohany 2015). We used WICKERsoft to finalize the classification of TE consensus sequences (Breen et al. 2010). Specifically, we used WICKERsoft to screen for copies of known consensus sequences from other fungal species with blastn filtering for sequence identity > 80% and sequence length > 80%. (Altschul et al. 1997). Then, using WICKERsoft, flanks of 10000 bp were added and visually inspected for sequence similarity and terminal repeats with dot plots. Subsequent multiple sequence alignments were performed with 10-15 sequences using ClustalW (Thompson, Higgins, and Gibson 1994). Alignment boundaries were visually inspected in WICKERsoft and trimmed if necessary. Using WICKERsoft, consensus sequences were classified according to the presence and type of terminal repeats, as well as homology of the encoded proteins based on blastx against the NCBI protein database. Consensus sequences were named according to the three-letter classification system (Wicker et al. 2007). The reference genome was annotated with the curated consensus sequences using RepeatMasker v. open-4.0.7 with a cut-off value of 250 (Smit, Hubley, and P. 2015). Simple repeats, low complexity regions and annotated elements shorter than 100 bp were filtered out and adjacent identical TEs overlapping by more than 100 bp were merged as belonging to the same TE family. Different TE families overlapping by more than 100 bp were considered as nested insertions and were renamed accordingly. Identical elements separated by less than 200 bp are indicative of interrupted elements and were grouped into a single element. TEs overlapping genes were recovered using the bedtools v. 2.27.1 suite and the “overlap” function (Quinlan and Hall 2010).</p>
Dataset: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Dataset used in the experiments of the publication: "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data" by Bach et al.</p> <p><strong>File description:</strong></p> <ul> <li> <p>cfmid4.tar: MS² spectra simulated using <a href="https://bitbucket.org/wishartlab/cfm-id-code/src/CFM-ID_4.0.7/">CFM-ID (v4.0.7)</a> for all molecular candidate structures</p> </li> <li> <p>db_layout.png: Visualization of the SQLite database (DB) layout</p> </li> <li> <p>massbank.sqlite.gz: DB containing all needed data to (re-)run the experiments shown in the paper. Please read "DB_README.md" for further details. The database file can be unpacked using gzip.</p> </li> <li> <p>metfrag.tar: MetFrag input files and MS² scores for all candidate sets computed using the <a href="https://ipb-halle.github.io/MetFrag/projects/metfragcl/">MetFrag software</a>.</p> </li> <li> <p>sirius_scores.tar: MS² scores for all candidates and measured spectra using the <a href="https://bio.informatik.uni-jena.de/software/sirius/">SIRIUS software</a>.</p> </li> <li> <p>sirius_inputs.tar: Input (ms-files) for the SIRIUS software.</p> </li> <li> <p>DB_README.md: Description of each table in the "massbank.sqlite" SQLite DB.</p> </li> <li> <p>db_processing_scripts.tar: Scripts to re-produce the "massbank.sqlite" and a README.md providing further information on the process.</p> </li> <li> <p>massbank__2020.11__v0.6.1.sqlite: Base SQLite DB from which the "massbank.sqlite" was build up. It was created using the "<a href="https://github.com/bachi55/massbank2db">massbank2db</a>" (v0.6.1) Python package using the <a href="https://github.com/bachi55/MassBank-data/tree/2020.11-branch">MassBank release 2020.11</a>.</p> </li> <li> <p>substructure_fingerprints.tar: Pre-computed substructure counting fingerprints for all candidates related to our experiments.</p> </li> </ul> <p><strong>Instructions:</strong></p> <p>The "massbank.sqlite" can be directly used with the Structure Support Vector Machine Model (SSVM) described in the manuscript and implemented in the "<a href="https://github.com/aalto-ics-kepaco/msms_rt_ssvm">ssvm</a>" Python package.</p> <p>If desired, the database can be re-produced using the scripts provided in "db_processing_scripts.tar":</p> <ol> <li>Create a directory for all data</li> <li>Download and extract the ... <ol> <li>Processing scripts</li> <li>MS² scorer outputs (e.g. metfrag.tar)</li> <li>Pre-computed substructure fingerprints</li> </ol> </li> <li>Follow the instructions given in the "README.md" of the "db_processing_scripts.tar"</li> </ol>
FIG. 8 in An annotated checklist of the birds of Burkina Faso
FIG. 8. — Gyps africanus Salvadori, 1865, Folonzo, Cascade Region, 10.II.2010 (photo M. Pavia). Relatively abundant up to the last century, many vultures are now classified as Critically Endangered.
FIG. 23 in An annotated checklist of the birds of Burkina Faso
FIG. 23. — Cecropis daurica domicella (Heuglin,1869), Karfiguela Falls, Banfora, Cascade Region, 14.III.2011 (photo M. Pavia).
FIG. 9 in An annotated checklist of the birds of Burkina Faso
FIG. 9. — Gyps rueppelli rueppelli (A. E. Brehm, 1852) juvenile, Folonzo, Cascade Region, 10.II.2010 (photo M. Pavia).
FIG. 15 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 15. — Emys orbicularis Linnaeus, 1758 (young). Both the colour and the long pointed tail may explain the Roman name mus (lit. "mouse"). Photo credit: Katya (CC BY-SA 2.0).
FIG. 11 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 11. — Some of the most popular ostrea (externally shelled molluscs). A, a date mussel (balanus); B, a mussel (musculus – also myax or mitulus); C, a scallop (pectunculus); D, a spiny dye murex (murex or purpura). Detail from a Roman mosaic from Pompeii, 1st century CE (Museo Archeologico Nazionale, Napoli; photo credit: A. Guasparri).
FIG. 7 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 7. — Two mollia (cephalopods), i.e. a sepia (bottom, left) and a polypus (center). Detail from a mosaic in Herculaneum (female thermae floor), Ist century CE. Photo credit: A. Guasparri.
FIG. 10 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 10. — The Roman folk-taxonomy of conchylium2 (i.e. mostly, our externally shelled molluscs). Abbreviations: LF, life-form; LF1+, sublife-form exceeding LF level by one more level; LF2+, sublife-form exceeding LF level by two more levels; LF3+, sublife-form exceeding LF level by three more levels; INT, intermediate;FG, folkgeneric; FS, folk-specific. Symbols: *, prototypical; /, synonymy; (…), ethnotaxonomic ascription only presumed, due to lack of explicit statements in the sources; + (superscript), multiple ethnotaxonomic ascription due to different statements in the sources;?, presumed folk taxon. See each entry in Appendix 1 for details.
FIG. 5 in The Roman classification and nomenclature of aquatic animals: an annotated checklist (with a focus on ethnobiology)
FIG. 5. — Channe (comber). The one depicted is Serranus scriba (Linnaeus, 1758), the painted comber. Detail from a Roman mosaic from Pompeii, 1st century CE (Museo Archeologico Nazionale, Napoli; photo credit: A. Guasparri).
ScienceDex guides
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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.