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FIGURE 50 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURE 50. Cladistic analysis of Toxotarsinae. Single most parsimonious tree recovered with TNT in the equal weighting analysis. Tree length = 102; CI=0.57; RI=0.71. Values above the branches indicate bootstrap (BS, left) and jackknife (JK, right) supports. Values below the branches indicate the absolute Bremer (BR, left) and relative fit difference (RFD, right) supports for each branch. * = node not recovered in bootstrap consensus tree.
FIGURES 42–49. Male terminalia. 42–44 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 42–49. Male terminalia. 42–44. Cerci and surstyli in posterior view (arrow showing dorsal region). 42. Sarconesia roraima. 43. S. chlorogaster. 44. S. magellanica. 45–47. Cercus and surstylus in lateral view. 45. S. roraima. 46. S. magellanica. 47. S. chlorogaster. 48–49. Pre- and postgonite (dotted area highlighting less sclerotized portion at apex of postgonite). 48. S. roraima. 49. S. dichroa (arrow showing seta on anterior margin of postgonite). Scale bars: 0.5 mm. Abbreviations: cer—cercus, sur—surstylus, prg—pregonite, pog—postgonite.
FIGURES 36–41 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 36–41. Ovipositor, ventral (left) and dorsal (right) views. 36. Calliphora nigribasis. 37. Sarconesia roraima. 38. S. dichroa. 39. N. chilensis. 40. S. chlorogaster. 41. Toxotarsus nigrocyaneus. Scale bar: 0.5 mm. Abbreviations: cer—cercus, epi—epiproct, hyp—hypoproct, st—sternite, tg—tergite.
FIGURES 31–33. Male abdominal sternites. 31. Sarconesia magellanica. 32. S. chlorogaster. 33 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 31–33. Male abdominal sternites. 31. Sarconesia magellanica. 32. S. chlorogaster. 33. Toxotarsus humeralis. Abbreviation: st—sternite.
FIGURES 27–28 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 27–28. Position of posterior region of lower calypter relative to body axis. 27. Divergent. 28. Not divergent.
FIGURES 10–18 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 10–18. Thoraces of Toxotarsinae showing position of setae on scutum and scutellum, dorsal view. 10. Sarconesia roraima. 11. S. magellanica. 12. N chilensis. 13. Sarconesia splendida. 14. S. versicolor. 15. S. dichroa. 16. S. chlorogaster. 17. Toxotarsus humeralis. 18. T. nigrocyaneus. Abbreviations: acr s—acrostichal setae, ap sctl s—apical scutellar setae, b sctl s—basal scutellar setae, dc s—dorsocentral setae, ds sctl s—discal scutellar setae, ial s—intra-alar setae, pal cal—postalar callus, pal s—postalar setae, pprn lb—postpronotal lobe, pprn s—postpronotal setae, presut sct—presutural scutum, psut sct— postsutural scutum, sctl—scutellum, spal s—supra-alar setae, trn sut—transverse suture.
FIGURES 1–6 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 1–6. Head characters of Toxotarsinae. 1–2. Parafacial setae. 1. Sarconesia chlorogaster. 2. S. magellanica. 3–4. Color of postgenal setae. 3. S. chlorogaster. 4. S. magellanica. 5–6. Apex of pedicel, highlighted by arrows. 5. Calliphora nigribasis. 6. S. chlorogaster. Abbreviations: gn—gena, pafc—parafacial setae, ped—pedicel, pgn—postgena.
FIGURES 25–26 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 25–26. Color of upper calypter rim, highlighted by arrows. 25. Pale (Sarconesia chlorogaster). 26. Dark (S. magellanica).
FIGURES 34–35. Female abdominal sternites. 34. Sarconesia roraima. 35. S in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 34–35. Female abdominal sternites. 34. Sarconesia roraima. 35. S. magellanica. Abbreviation: st—sternite.
FIGURES 7–9 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 7–9. Antennae of Toxotarsinae. 7. Sarconesia roraima. 8. S. chlorogaster. 9. Toxotarsus humeralis.
FIGURES 29–30 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 29–30. Median longitudinal stripe on abdomen. 29. Absent (Sarconesia roraima). 30. Present (S. chlorogaster); arrow showing stripe.
FIGURE 52 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURE 52. Cladistic analysis of Toxotarsinae. Single most parsimonious tree recovered with TNT in the implied weighting analysis (k = 1.3281). Tree length = 104; CI=0.577; RI=0.718. Values above the branches indicate bootstrap (BS, left) and jackknife (JK, right) supports. Values below the branches indicate the absolute Bremer (BR, left) and relative fit difference (RFD, right) supports for each branch.
FIGURES 19–24 in First cladistic analysis of Toxotarsinae (Diptera: Calliphoridae), with insights on the evolution of the group and on the transformation series of some historically controversial characters
FIGURES 19–24. Thoracic characters of Sarconesia chlorogaster and outgroup Chrysomya megacephala. 19–20. Pruinosity of scutellum. 19. C. megacephala. 20. S. chlorogaster. 21–22. Setae on ventral surface of stem of R vein, shown by arrows. 21. Absent (C. megacephala). 22. Present (S. chlorogaster). 23–24. Disposition of setae on dorsal surface of R 4+5 vein, highlighted by arrows. 23. From stem halfway to crossvein r-m (C. megacephala). 24. Only on stem (S. chlorogaster). Abbreviations: psut sct—postsutural scutum, sctl—scutellum, R 4+5 —vein R 4+5, r-m—crossvein r-m.
FIGURE 3. Rubus lentianus. A, B in Rubus lentianus, a new species from Austria and Germany-another piece of the puzzle from a European hotspot of Rubus series Radula
FIGURE 3. Rubus lentianus. A, B: middle section of first-year stem with leaves; C: leaf of first-year stem (upper side); D: leaf of first-year stem (lower side); E: section of first-year stem; F: inflorescence; G: part of inflorescence with a close-up of a flower; H: young collective fruit. Austria, Upper Austria, A–G: Tarsdorf, 7 July 2021, H: Aschach a. d. Donau, 7 July 2020, phot. G. Király.
FIGURE 2 in Rubus lentianus, a new species from Austria and Germany-another piece of the puzzle from a European hotspot of Rubus series Radula
FIGURE 2. Rubus lentianus: A: section of leafed first-year stem; B: detail of first-year stem; C: margin of terminal leaflet; D: infructescence; E: inflorescence axis; F: peduncle; G: flower; H: petal; I: young carpel. Del. J. Táborská.
FIGURE 2. Himerometroidea proximal ray base series. A in Phylogeny and taxonomy of Himerometroidea (Echinodermata: Crinoidea)
FIGURE 2. Himerometroidea proximal ray base series. A. Catoptometra rubroflava (AH Clark, 1907), Tokyo Museum E3797. B. Himerometra grandis A. H. Clark, 1908 (= Dichrometra grandis (AH Clark, 1908)), Holotype Copenhagen CRI-17 C. Antedon spicata Carpenter 1881 (=Stephanometra indica (Smith, 1876), Holotype Leiden 1781 (currently: Dichrometra spicata (Carpenter, 1881), RMNH.ECH.1793). D. Alecto flagellata Müller, 1841, Holotype Leiden 1784 (currently: Dichrometra flagellata (Müller, 1841), RMNH.ECH.1784). E. Mariametra vicaria (Bell, 1894), ray bases showing finely spinose flanks of brachitaxes ossicles, holotype, NHM 93.8.25.91. F. Antedon laevicirra Carpenter 1881, Holotype Leiden 1794. G. Homalometra denticulata Holotype BMNH 1888.11.9.29. H. Actinometra robustipinna Carpenter 1881 Holotype Leiden 1772 (= Himerometra robustipinna (Carpenter 1881)). I. Colobometra verpretum AH Clark 1909, Holotype Copenhagen CRI-47. J. Antedon hartlaubi AH Clark, 1907, Holotype USNM 22606.
Unlabeled Sentinel 2 time series dataset (training, T30TXT): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p> <strong> T30TXT unlabeled S2 dataset </strong></p> <p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TXT</strong> are available. To download the full pretraining dataset, see : <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
GNSS time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)
<p>GNSS raw time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)</p> <p>The raw GNSS time series are available for each three GNSS stations we focus on in this study (COYU, IGUA and MECZ), as well as the three distant stations use to correct from common mode (INEG, TAMP and UXAL), at both 24-hour and 6-hour sampling rates.<br> Each file contains the date, East, North and Vertical displacement and associated errors in meters (format: Decimal year, E (m), N (m), U (m), dE (m), dN (m), dU (m)).</p>
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TYS): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYS</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
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.