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Fig. 4 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 4. Results of the PCA analysis for the two samples collected in the Mogyoróskuti meadows in 1999 and in 2001; i.e. temporalvariation within a population. The points represent the genotypic
Fig. 3 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 3. Results of the PCA analysis for the three distinct populations; i.e. spatial variation (Karst region: Mogyoróskuti meadows, Haragistya; Zemplén Mts.: Gyertyánkúti meadows). The points represent
Fig. 1 in Temporal And Spatial Pattern Of Genetic Differentiation In Isophya Kraussi (Orthoptera: Tettigonoidea) In Ne Hungary
Fig. 1. Sample sites. Aggtelek Karst region: Haragistya near Aggtelek (1), Mogyoróskuti meadows near Jósvafő (2); Zemplén Mts.: Gyertyánkúti meadows near Telkibánya (3)
Fig. 1 in Spatio-Temporal Variations Of Macroinvertebrate Community In The Tisza River (Ne Hungary)
Fig. 1. Cross-section of the Tisza riverbed between Tiszamogyorós and Lónya at low water level. Abbreviations: 1 = main flow (right bank), 2 = mid-bed (at half distance between the right and the left
Fig. 3 in Spatio-Temporal Variations Of Macroinvertebrate Community In The Tisza River (Ne Hungary)
Fig. 3. Classification of samples from the cross-section of River Tisza between Lónya and Tiszamogyorós by dates. All taxa were taken into consideration with chironomids identified to family level. Groups are as follows: 1: March, 2: April, 3: June, 4: July, 5: September, 6: November
Fig. 2 in Spatio-Temporal Variations Of Macroinvertebrate Community In The Tisza River (Ne Hungary)
Fig. 2. Classification of sampling sites along the cross-section of River Tisza between Lónya and Tiszamogyorós. All taxa were taken into consideration with chironomids identified to species level. Groups are as follows: 1: left bank, 2: mid-bed, 3. main flow
Fig. 1 in Spatial, Temporal And Individual Variability In The Autumn Diet Of European Hare (Lepus Europaeus) In Hungary
Fig. 1. Localities of the study areas. Study areas are shown as gray patches, the capital (Budapest) by striped gray area, Lake Balaton and Lake Tisza by black ones. Black lines are Hungarian rivers and
ORBITAAL: cOmpRehensive BItcoin daTaset for temporAl grAph anaLysis
<h3>Dataset Construction</h3> <p>This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (<a href="https://github.com/blockchain-etl/bitcoin-etl">https://github.com/blockchain-etl/bitcoin-etl</a>) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by <a href="https://www.walletexplorer.com/">https://www.walletexplorer.com/</a></p> <p>[1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," <em>2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)</em>, Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.<br>keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain},</p> <p> </p> <h3>Dataset Description</h3> <p><strong>Bitcoin Activity Temporal Coverage</strong>: From 03 January 2009 to 25 January 2021</p> <h4>Overview:</h4> <p>This <strong>dataset </strong>provides a <strong>comprehensive</strong> representation of <strong>Bitcoin exchanges</strong> between entities over a s<strong>ignificant temporal span</strong>, spanning from the inception of Bitcoin to recent years. It encompasses <strong>various temporal resolutions</strong> and <strong>representations</strong> to <strong>facilitate Bitcoin transaction network analysis </strong>in the context of <strong>temporal graphs</strong>.</p> <p>Every dates have been retrieved from bloc UNIX timestamp and GMT timezone.</p> <h4>Contents:</h4> <p>The dataset is distributed across three compressed archives:</p> <p>All data are stored in the <strong>Apache Parquet file format</strong>, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package.</p> <ol> <li> <p><strong>orbitaal-stream_graph.tar.gz</strong>:</p> <ul> <li>The root directory is <em>STREAM_GRAPH/</em></li> <li>Contains a <strong>stream graph</strong> representation of Bitcoin exchanges at the <strong>finest temporal scale</strong>, corresponding to the validation time of <strong>each block</strong> (averaging approximately 10 minutes).</li> <li>The stream graph is divided into 13 files, one for each year</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet,</strong> where <em>[YYYY]</em> stands for the corresponding <em>year</em> and <em>[ID]</em> is <em>an integer</em> from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>STREAM_GRAPH/EDGES/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-all.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>snapshot</strong> network representing <strong>all transactions aggregated </strong>over the whole dataset period (from Jan. 2009 to Jan. 2021).</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-all.snappy.parquet</strong>.</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/ALL/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-year.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>yearly</strong><em> </em>resolution of <strong>snapshot</strong> networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet</strong>, where <em>[YYYY]</em> stands for the corresponding <em>year </em>and <em>[ID]</em> is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/year/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-month.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>monthly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY] </em>and <em>[MM] </em>stands for the corresponding <em>year </em>and <em>month, </em>and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/month/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-day.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>daily </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, and <em>[DD] </em>stand for the corresponding <em>year</em>, <em>month</em>, and <em>day</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/day/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-hour.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>hourly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, <em>[DD]</em>, and <em>[hh]</em> stand for the corresponding <em>year, month, day, </em>and <em>hour</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/hour/</em></li> </ul> </li> <li> <p><strong>orbitaal-nodetable.tar.gz</strong>:</p> <ul> <li>The root directory is <em>NODE_TABLE/</em></li> <li>Contains two files in parquet format, the first one gives <strong>information </strong>related to <strong>nodes </strong>present in stream graphs and snapshots such as <strong>period of activity</strong> and associated global <strong>Bitcoin balance</strong>, and the other one contains the list of <strong>all associated Bitcoin addresses.</strong></li> </ul> </li> </ol> <p> </p> <p>Small samples in CSV format</p> <ol> <li> <p><strong>orbitaal-stream_graph-2016_07_08.csv</strong> and <strong>orbitaal-stream_graph-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to stream graph representations of an halvening happening in 2016.</li> </ul> </li> <li> <p><strong>orbitaal-snapshot-2016_07_08.csv </strong>and<strong> orbitaal-snapshot-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to daily snapshot representations of an halvening happening in 2016.</li> <li><strong> </strong></li> </ul> </li> </ol> <p> </p> <p> </p> <p> </p>
FIGURE 11. A–B in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 11. A–B: Pearson correlations between the 14 variables and the two PCO axes based on P. leo, P. spelaea, and P. fossilis (thus excluding P. atrox, corresponding to Figure 10B).
FIGURE 10. A in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 10. A: Principal coordinate analyses based on 14 log-transformed morphometric variables. The first axis is parallel with an overall increase in cranial length. Across its whole range, P. spelaea differs from P. fossilis, and P. fossilis is clearly distinct from P. leo. However, P. spelaea from the Western Europe alone does not differ from P. fossilis. B: Principal coordinate analyses based on 14 log-transformed morphometric variables, excluding P. atrox. This analysis shows that P. spelaea from the Western Europe and the Eastern Europe–Asia do not fully overlap in morphospace. The first PCO axis in both analyses correlates strongly positively with all variables and thus reflects the size axis. Specimen numbers: P. atrox (North America): 1–Ichetucknee, 2–Natural Trap Cave, 3–9–Rancho la Brea; P. fossilis: 10–Mauer (Germany), 11–Azé I-3 (France); P. intermedia: 12–Romain-la-Roche (France), 13–Vence (France), 14–Edingen (Germany), 15–Niedźwiedzia Cave (Poland), 16–San (Poland); P. spelaea: 17–Bottrop (Germany), 18– Huttenheim (Germany), 19–Perickhöhlen (Germany), 20–Siegsdorf (Germany), 21–Zoolithenhöhle (holotype; Germany), 22–24–Zoolithenhöhle (Germany), 25–Zandobbio (Italy), 26–Brno (Czech R.), 27–Sloup (Vienna specimen; Czech R.), 28–Sloup (OK 130570, Czech R.), 29–Srbsko–Chlum Komín Cave (Czech R.), 30–Výpustek 1 (Czech R.), 31–Výpustek 3 (Czech R.), 32–Igrita (Croatia), 33–Ursilor (Romania), 34–Binagady (Caucasus, Georgia), 35–Desna (Russia), 36–37–Isa River (South Ural, Russia), 38–Kondakovka (Russia), 39–Mokhokho (Russia), 40–Uzhur (Central Siberia), 41–42–Medvedia Cave (Slovakia); P. leo (Africa and India): 43–50.
FIGURE 8 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 8. Bivariate relationships between the cranial length and nine morphological variables. The lines represent slopes of the relationship between the log-transformed cranial length and nine variables fitted by reduced major axis regression separately for P. fossilis, P. spelaea, and P. leo. Axes are logged.
FIGURE 9 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 9. The summary of mean allometric coefficients (slopes of the relationship between the log-transformed cranial length and nine variables fitted by reduced major axis regression) with 95% bootstrapped confidence intervals. They show more positive allometry for mastoid width, canine width, medial nasale length, and lateral nasal length in P. fossilis relative to other three species (although 95% confidence intervals are broad). The postorbital width shows negative allometry in P. leo whereas it shows positive allometry in P. spelaea and P. fossilis. The missing values in P. atrox did not allow to measure its allometric coefficients for lateral nasal length.
FIGURE 6 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 6. Different types of lion heads in Paleolithic art, maybe indicating the cranial profile type 2 (A), probably the cranial profile type 1 (B), and the cranial profile type 0 (C-F). A: a lion head sculpture from Kostenki near Voronezh, Russia (ca. 23 ka BP, redrawn from Efimenko 1958); B: a lion head sculpture from Vogelherd, Germany (ca. 38-33 ka BP, redrawn from Koenigswald and Schmitt 1987); C: a lion head sculpture from Dolní Věstonice, Moravia–Czech Republic (ca. 27-29 ka BP, redrawn from Jelínek 1972); D: a cave lion depicted in La Marche, France (Magdalenian); E: cave lion pair depicted in Chauvet, France (˂ 26 ka BP); F: cave lions depicted in Chauvet, France (˂ 26 ka BP, redrawn from Clottes 2001). © J. Gullár, 2009-2014 (A–C, F) and the archive of M. Sabol (D and E). Author of illustrations A–C and F is J. Gullár (please, cite it as: J. Gullár in Sabol et al., 2022); D and E are from the archive of M. Sabol.
FIGURE 5 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 5. Boxplots showing differences in cranial length and in the POC/IOB ratio. The horizontal dashed lines approximately span the minima and maxima in P. leo from Mazák (2010).
FIGURE 4 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 4. The types of lion crania distinguished based on cranial profile with the assumed life reconstruction. A: cranium with the straight nasofrontal profile (0)–cranium of Panthera spelaea from the Medvedia jaskyňa Cave in the Západné Tatry Mts. in Slovakia (Last Glacial), B: cranium with the intermediate profile between type 0 and type 2 (1)– cranium of Panthera spelaea from the Zoolithenhöhle Cave in Germany (Last Glacial), C: cranium with the concave nasofrontal profile (2)–cranium of Panthera fossilis from Azé in France (Holsteinian). © J. Gullár, 2011-2021. The crania are not scaled. Author of illustrations is J. Gullár (please, cite it as: J. Gullár in Sabol et al., 2022).
FIGURE 1 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 1. Chronology of lion lineages based on the data from the fossil record and molecular and paleogenetic researches, referred in the "State of art" section. The grey color in the Panthera spelaea lineage suggests questionable evolutionary position of these lion forms. American lions probably represent a separate lineage. The Quaternary chronostratigraphy system is created with the TimeScale Creator software, v. 8.0 (2021).
FIGURE 2. The 12 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 2. The 12 linear cranial characters measured on lion crania. 1: greatest cranial length (L, measured as the distance between the prosthion and the acrocranion), 2: palatal length (LP, measured parasagittally as the distance between the prosthion and the staphylion), 3: medial length of nasals (LMN, measured parasagittally as the distance between the naso-frontal suture and the dorsal margin of the external narial opening), 4: lateral length of nasals (LLN, measured as the distance between the naso-frontal suture and the anteriormost tip of the nasal), 5: greatest nasal width (BN, measured at rostral projection of nasals), 6: the snout width (BS, measured at the level of upper canines), 7: interorbital width (IOB, distance between orbits), 8: maximum cranial width across zygomatic arches (BZ), 9: width across postorbital constriction (POC), 10: greatest neurocranial width (BNC, greatest distance between lateral margins of braincase: euryon–euryon), 11: mastoid width (BM, measured across mastoid processes), and 12: greatest diameter of the auditory bullae (LAB). Cranium drawing modified according to Argant (1991).
FIGURE 3 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 3. Profile depths (blue) and angular variables (red) measured on lion crania. Angle A: the cranial profile angle (nasale–frontale angularity), angle B: the angle between narial aperture and nasofrontal profile (premaxillary– nasofrontale angularity), and angle C: the angle between alveolar margin and postorbital process (maxillary–frontale angularity). Cranium drawing modified according to Argant (1991).
Figure 13 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 13. – Indicator species for each cluster of the dendrogram resulting from the self-organizing map procedure (n = 10). IndVal values (in %) are shown in brackets. Shown indicator values (p <0.05) are only those greater than 25%.
Figure 2 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 2. – Variation (average and standard deviation) in species richness by sampling sites. Site order follows the upstream-downstream gradient.
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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.