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Data for "Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats"

<p>Database as tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2024) Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats. <em>Mammal Review.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/melissameierhofer/Meta-5-Rule.git</p>

opencc-by-nc-sa-4.0Jul 2021View details →

Trove tag counts

<p>This dataset was derived from the full harvest of <a href="../doi/10.5281/zenodo.5094313">Trove public tags</a>. It contains a list of unique tags and the total number of resources in Trove each tag is attached to. It is formatted as a CSV file with the following columns:</p> <ul> <li>`tag` – the tag string</li> <li>`count` – number of resources the tag has been applied to</li> </ul> <p>User content added to Trove, including tags, is available for reuse under a CC-BY-NC-SA licence.</p>

opencc-by-4.0Jun 2024View details →

Specialised POS Tagged Syriac Corpus for State Morphology

<h1>Overview</h1> <p>A total of twelve .TXT files each representing a Syriac text that has been transcribed and tagged for part-of-speech (POS). This corpus forms part of a PhD research project on the historical syntax of Aramaic (Syriac) at The Australian National University (2020—current) in Canberra, Australia. This research project is interested in noun state morphology, among other topics, which is reflected in the POS scheme for this corpus.</p> <h2>Method</h2> <p>A detailed summary of this methodology is provided in El-Khaissi (data paper in review with the <em>Journal of Open Data Humanities</em>).</p> <ul> <li>Transcriptions are sourced from <a href="https://syriaccorpus.org/" target="_blank" rel="noopener">Digital Syriac Corpus.</a></li> <li>POS tags are based on word matches using <a href="https://sedra.bethmardutho.org/about/openapi" target="_blank" rel="noopener">SEDRA IV API (v1.0.0).</a></li> <li>Selection of Syriac texts was optimised to minimise external influence on Syriac grammar and maximise full coverage of key periods of the Syriac language from 2nd—13th century AD.</li> </ul> <h2>POS Format & Abbreviations </h2> <p>POS tags in the text files follow the following format:</p> <blockquote> <p><syntax-category>-<state>_<syriac_word></p> </blockquote> <p>Thus, an underscore '_' marks the beginning of a tag sequence while tag values are separated by hyphen(s) '-'. For example (noting text directionality constraints):</p> <blockquote> <pre>ܒܘܪܟܬܐ_EMP-N</pre> </blockquote> <p>The following abbreviation lists the definition of all POS tags, which are based on the parameters available in <a href="https://sedra.bethmardutho.org/about/openapi" target="_blank" rel="noopener">SEDRA IV API (v1.0.0).</a></p> <table> <tbody> <tr> <td>Absolute state noun (indeterminate relic)</td> <td>ABS</td> </tr> <tr> <td>Emphatic state noun (new indeterminate)</td> <td>EMP</td> </tr> <tr> <td>Construct state noun (bound noun)</td> <td>CNS</td> </tr> <tr> <td>State not applicable</td> <td>X</td> </tr> <tr> <td>particle</td> <td>PTCL</td> </tr> <tr> <td>pronoun</td> <td>PRO</td> </tr> <tr> <td>preposition</td> <td>PREP</td> </tr> <tr> <td>verb</td> <td>V</td> </tr> <tr> <td>denominative</td> <td>DEN</td> </tr> <tr> <td>noun</td> <td>N</td> </tr> <tr> <td>numeral</td> <td>NUM</td> </tr> <tr> <td>substantive</td> <td>SBV</td> </tr> <tr> <td>adjective</td> <td>ADJ</td> </tr> <tr> <td>proper noun</td> <td>PN</td> </tr> <tr> <td>adverb</td> <td>ADV</td> </tr> <tr> <td>demonym</td> <td>DNM</td> </tr> <tr> <td>participle adjective</td> <td>PTCPADJ</td> </tr> <tr> <td>adverb</td> <td>ADV</td> </tr> <tr> <td>idiom</td> <td>IDM</td> </tr> <tr> <td>See Quality Control & Limitations below</td> <td>DUP</td> </tr> </tbody> </table> <h2>Quality Control & Limitations</h2> <p>On average per manuscript, the POS-tagging process achieved a 63.13% saturation of texts. The POS tagging process was based on an exact-match process, which does not take into account syntactic or semantic context. Syriac words which exhibit homonymy are thus tagged with the value 'DUP' and should be assessed manually based on its original context. Among all 297,981 words in the corpus with an available POS tag, approximately 73,188 (24.56%) of tags reflected some kind of homonymy involving a word with various semantic and/or syntactic interpretations.</p> <p>Since this dataset was created as part of a research project investigating noun state morphology, additional tags were created targetting various state values. Grammatical elements, like number and gender, were not required as part of this investigation and therefore excluded from the POS-tagging process.</p> <h2>Contact</h2> <p>For any questions, please contact Charbel El-Khaissi <Charbel.El-Khaissi@anu.edu.au>.</p>

Figure 5 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry

Figure 5. KEGG pathways of the differentially expressed phosphorylated proteins. The abscissa indicates the first 10 significantly enriched KEGG pathways and the ordinate indicates the significance of enriched KEGG pathways, the more left, the more significant.

Figure 4 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry

Figure 4. Gene ontology annotations of the differentially expressed phosphorylated proteins. The abscissa indicates the enriched GO functional classification, including biological process (A), cellular component (B), and molecular function (C). The ordinate indicates the size of the significance of corresponding to each entry, the more left, the more significant.

Figure 3 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry

Figure 3. Clustering heatmap of different expression phosphorylated peptides. Each row represents a phosphorylated peptide segment, each column represents a group of samples. The logarithmic value (logarithmic transformation based on 2) of the significantly differentially expressed phosphorylated peptides in different samples is displayed in the clustering heatmap in different colors. Red represents significant upregulation of phosphorylated peptides; blue represents significant down-regulation of phosphorylated peptides.

Figure 2 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry

Figure 2. Volcano plots from different group comparisons. The abscissa indicates difference multiple (logarithmic transformation based on 2), the ordinate indicates the significant of difference (logarithmic transformation based on 10). The red point is significantly upregulated phosphorylated peptide segment, the blue point is significantly downregulated phosphorylated peptide segment and the gray point is a phosphorylated peptide segment with no significant difference.

opencc-by-4.0Jul 2024View details →

An unambiguous POS tagging set

<p>This data set contains 1,123 short, POS-tagged sentences (extracted from an earlier data set; see https://zenodo.org/records/7694423), using the Universal tag set. The sentences can easily be POS tagged by a human tagger. However, standard POS taggers struggle with these sentences. The data file contains a header row that describes each column. The first column indicates the type of sentence (either a transcript of spoken text (0) or a sentence originating from written text (1)), the second column contains the actual sentence, with ground truth POS tags. The third column indicates the index of the mistagged token, and the remaining five columns show the tags assigned (of which at least one is a mistagging) of five different taggers.</p>

Fig. 4 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 4. Sequence alignment of putative lectin from Scorpaena plumieri. Alignment of a lectin-like EST in silico translated sequence from S. plumieri (ClustalW2 EBI) with fish-egg lectin from Oplegnathus fasciatus (BAL618145), Dicentrarchus labrax (CBK52298), Maylandia zebra (XP_004574029), and Oreochromis niloticus (XP003443389). The recombinant clone was isolated with antibody fraction derived from S. plumieri venom. * identifies and identical residue;: identifies a conserved residue. Underlined residues represent invariable sites, underlined IRLS = N-acetylation site.

Fig. 3 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 3. The classification of EST from Scorpaena plumieri based on their putative fractions. Three-hundred fifty-six EST edited sequences were initially analyzed with Blast and Swiss protein databanks. The consensus sequence was attributed a function based on the strongest match.

Fig. 2 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 2. Agarose gel electrophoresis of DNA isolated from clones. White colonies containing insert were grown and the plasmidial DNA isolated and digested with EcoRI enzyme. An aliquot from each clone (1-27) was electrophoresed on 1% Agarose gel and stained with ethidium bromide.

Fig. 1 in Expressed sequence tags in venomous tissue of Scorpaena plumieri (Scorpaeniformes: Scorpaenidae)

Fig. 1. Agarose- formaldehyde electrophoresis of RNA from Scorpaena plumieri. A) 1) 2 µg of E. coli tRNA; 2) 2 µg de rRNA de Rattus norvegicus; 3) and 4) 2 µg total RNA from S. plumieri spine gland. B) 1) 2 µg de total RNA from S. plumieri; 2) the same sample incubated 2 h a 37ºC before electrophoresis.

Figure 9 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 9. Simulation results for the lift forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 8 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 8. Simulation results for the drag forces acting on the Model A and B tags (top panel) in constant flow (5.6 m/s) as a function of orientation (–20º to 180º). Measured results as a function of orientation (–20º to 90º) in constant 5.6 m/s flow are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 4. CFD simulation results for Models A in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 4. CFD simulation results for Models A (panels A and C) and B (panels B and D) in steady 5.6 m/s flow. The blue, yellow, and green regions are areas of reduced flow speed that generate forces on the tags. The upper panels show the flow speed over a horizontal cross-section at the tag midline. The lower panels show flow speed over a vertical cross-section at the centerline of the tag. The improved flow around Model B is evident in the smaller magnitude of blue coloration in the wake behind the tag.

Figure 2 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 2. An illustration of the Model A tag in the computational domain used for the simulations of all tag designs. The fluid flow is from left to right and representative orientations of the tag to the flow are shown at the bottom of the figure.

Figure 7 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 7. Simulation results for the lift forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).

Figure 10 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 10. Experimental load testing of a Model A tag attached at three test sites on a common dolphin cadaver using four silicone suction cups. Applied force vs. calculated total cup attachment force at the four silicone suction cups are shown for lift (bottom panel) and drag (top panel) loading. The average pressure difference for the four cups is also shown on the right hand axis, where 1 atmosphere is approximately 100 kPa. Forces were applied via a line attached to the tag by pulling either perpendicular to the body (lift) or parallel (drag). The curves end where the cup attachment failed or the cups began to slide. The average of two trials at each site is shown in each panel.

Figure 6 in Drag of suction cup tags on swimming animals: Modeling and measurement

Figure 6. Simulation results for the drag forces acting on the Model A and B tags (top panel) in variable flow (0.25–10 m/s) with fixed orientation (0º). Measured results in variable flow speed (1–5.6 m/s) are compared to simulations for the Model A tag (bottom left panel) and Model B tag (bottom right panel).