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FIGURE 6 in Caesio xanthalytos, a new species of fusilier (Perciformes: Caesionidae) from the Western Indian Ocean, with records of range extensions for several species of Caesionidae
FIGURE 6. (A) Caesio lunaris, 185 mm SL, Sodwana Bay, SAIAB 97030 (Phil Heemstra); (B) Caesio xanthonota, 240 mm SL, Sodwana Bay, SAIAB 187724 (Phil Heemstra); (C) Caesio xanthonota underwater, + C. xanthalytos and Pseudanthias cooperi, Cape Vidal (28˚9.775' S, 32˚34.045 E), March 2009 (Dennis King); (D) Dipterygonotus balteatus, Sodwana Bay, August 2011 (ADC); (E) Pterocaesio marri, 72 mm SL, northern Mozambique, SAIAB 82460 (Phil Heemstra).
FIGURE 3 in Caesio xanthalytos, a new species of fusilier (Perciformes: Caesionidae) from the Western Indian Ocean, with records of range extensions for several species of Caesionidae
FIGURE 3. Caesio xanthalytos sp.nov., holotyope, SAIAB 56575, 177 mm SL, KwaZulu-Natal, South Africa (Drawn by Elaine Heemstra).
FIGURE 1 in Caesio xanthalytos, a new species of fusilier (Perciformes: Caesionidae) from the Western Indian Ocean, with records of range extensions for several species of Caesionidae
FIGURE 1. Supratemporal scale band of (A) Caesio caerulaurea, 'V'-notched, and (B) C. xanthalytos, confluent.
FIGURE 68. S. pannonicus adult. Several setae and structures omitted. 68, trachea leg II in Morphological analysis of the oribatid mite species Scutovertex pannonicus Schuster and description of its juvenile stages (Acari: Oribatida: Scutoverticidae)
FIGURE 68. S. pannonicus adult. Several setae and structures omitted. 68, trachea leg II. Same number = same individual; ' and " = left and right side. Variations in length of long branch of femoral trachea leading into tibia or tarsus (0, 1, 2, 4, 4'); variations in length of short branch of femoral trachea leading around femur (0', 0'', 3, 5). 69, trachea leg III. Tarsus and distal half of tibia: variations of length of long branch of femoral trachea (1–4) leading into tibia or tarsus.
Data from: The effect of climate change on forest fire danger and severity in the Canadian boreal forests for the period 1976–2100
<p>Recent climatic trends have increased forest fire activity in Canada. This study aimed to evaluate how forest fire conditions might evolve across the Canadian borael forests in the future and to inform discussion about the impact of climate change on fire danger and severity.</p>
Data from: Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding
<p>Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily Bromus inermis, a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.</p>
Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity
<p><strong>Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity '</strong><br><strong>Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.nbd.2023.106382</span></a></strong></p> <p><strong>Dataset description</strong></p> <p><em>Data\data2publish\sub- </em>: 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs,<em> </em>Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal </p> <p><em>Data\data2publish\EA_info.xlsx</em>: number of epileptiform activities automatically detected for each animal at d28 and d29</p> <p><em>Data\data2publish\derivatives\eeg_preprocessing: </em>results of the script A_EEG_preprocessing.m, for each animal and session</p> <p><em>Data\data2publish\derivatives\elec_layout: </em>different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4</p> <p><em>Data\data2publish\derivatives\network_metrics</em>_<em>wpli: </em>results of network analyses (script C_network_analyses.m)</p> <p><em>Data\data2publish\derivatives\wpli</em>: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit</p> <p><strong>Code for analyses available here: </strong> <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a></p> <p>Abbreviations:</p> <p>EEG= electroencephalography</p>
Solar wind parameters and geomagnetic indices for severe (SYM-H < -100nT) geomagnetic storms over two solar cycles
<p><em><strong>Severe storms data set (1996-2022)</strong></em></p> <p><em>Lotz, Grant, Davel (2024).</em></p> <p><strong>Changes from previous version:</strong></p> <ul> <li>Previous version had errors in calculation of `HER_eh` and `ABK_eh`. The mistake is corrected in this version.</li> <li>This version includes storms from 1996-2022. Previous version had 1996-2019.</li> </ul> <p>This data set is curated from 1-minute time resolution OMNI [4] and INTERMAGNET [7] data, spanning the period 1996 - 2022.</p> <p>It consists of all geomagnetic storms that reached minimum SYM-H (Symmetric-H index) of < -100 nT. We classified these as "severe" storms. There are 115 such events in the period 1996 - 2018. No severe storms detected between 2007-2010 or in 2019. Each file in the set contains the events in that year (YYYY) in the format "severe_YYYY.pickle". The geomagnetic storms are identified by the procedure described in [1], with thresholds -100 nT and -20 nT (see [1] for further detail).</p> <p>The data sets are Pandas "dataframes" [2], saved in the Python "pickle" format [3]. Each dataframe contains 23 variables, listed in the table below, with their description and unit.</p> <p>Fourteen of the parameters are solar wind plasma and magnetic field parameters from the High Resolution OMNI data set [4].</p> <p>There are 7 geomagnetic variables:</p> <ul> <li>one is the global Sym-H index,</li> <li>four are H-component geomagnetic observations from Hermanus, South Africa (HER: 34.4 S, 19.22 E) and Abisko, Sweden (ABK: 68.35 N, 18.82 E) and their time derivatives</li> <li>two are the e_h index [5] calculated at HER and ABK</li> </ul> <p>Finally, we list the time shift applied to each data point to shift to the bow shock nose (described for OMNI at [6]) and a string identifier for each geomagnetic storm.</p> <p> </p> <table> <tbody> <tr> <td><strong>#</strong></td> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>1</td> <td>time_shift</td> <td> <p>Time shift applied to shift solar wind parameters to bow shock</p> </td> <td>s</td> </tr> <tr> <td>2</td> <td>Bt</td> <td>Magnitude of interplanetary magnetic field (IMF)</td> <td>nT</td> </tr> <tr> <td>3</td> <td>Bx</td> <td>X-component of IMF</td> <td>nT</td> </tr> <tr> <td>4</td> <td>By_GSE</td> <td>Y-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>5</td> <td>Bz_GSE</td> <td>Z-component of IMF (GSE coordinates)</td> <td>nT</td> </tr> <tr> <td>6</td> <td>By_GSM</td> <td>Y-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>7</td> <td>Bz_GSM</td> <td>Z-component of IMF (GSM coordinates)</td> <td>nT</td> </tr> <tr> <td>8</td> <td>Vsw</td> <td>Bulk solar wind speed</td> <td>km/s</td> </tr> <tr> <td>9</td> <td>Vx</td> <td>X-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>10</td> <td>Vy</td> <td>Y-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>11</td> <td>Vz</td> <td>Z-component of solar wind</td> <td>km/s</td> </tr> <tr> <td>12</td> <td>Np</td> <td>Proton number density in solar wind plasma</td> <td>#/cc</td> </tr> <tr> <td>13</td> <td>Temp</td> <td>Temperature of solar wind plasma</td> <td>K</td> </tr> <tr> <td>14</td> <td>Pd</td> <td>Dynamic or flow pressure of solar wind plasma</td> <td>nPa</td> </tr> <tr> <td>15</td> <td>E_field</td> <td>Solar wind electric field</td> <td>mV/km</td> </tr> <tr> <td>16</td> <td>SymH</td> <td>Symmetric-H index of the geomagnetic field</td> <td>nT</td> </tr> <tr> <td>17</td> <td>HER_H</td> <td>H-component of the geomagnetic field at Hermanus</td> <td>nT</td> </tr> <tr> <td>18</td> <td>HER_dHdt</td> <td>Time derivative of HER_H</td> <td>nT/min</td> </tr> <tr> <td>19</td> <td>HER_eh</td> <td>e_h index at HER</td> <td>nT/min</td> </tr> <tr> <td>20</td> <td>ABK_H</td> <td>H-component of the geomagnetic field at Abisko</td> <td>nT</td> </tr> <tr> <td>21</td> <td>ABK_dHdt</td> <td>Time derivative of ABK_H</td> <td>nT/min</td> </tr> <tr> <td>22</td> <td>ABK_eh</td> <td>e_h index at ABK</td> <td>nT/min</td> </tr> <tr> <td>23</td> <td>Storm_ID</td> <td>String identifier of the geomagnetic storm</td> <td>-</td> </tr> </tbody> </table> <p> </p> <p><em><strong>References</strong></em></p> <ol> <li>Lotz, S. I., & Danskin, D. W. (2017). <em>Space Weather</em>, 15, 1347– 1356. <a href="https://doi.org/10.1002/2017SW001662">https://doi.org/10.1002/2017SW001662</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/ow_min.html">https://omniweb.gsfc.nasa.gov/ow_min.html</a></li> <li><a href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li> <li><a href="https://docs.python.org/3/library/pickle.html">https://docs.python.org/3/library/pickle.html</a></li> <li>Wintoft, P., Wik, M., & Viljanen, A. J. Space Weather Space Clim., 5, A7 (2015). <a href="http://dx.doi.org/10.1051/swsc/2015008">http://dx.doi.org/10.1051/swsc/2015008</a></li> <li><a href="https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift">https://omniweb.gsfc.nasa.gov/html/ow_data.html#time_shift</a></li> <li><a href="https://www.intermagnet.org/index-eng.php">http://www.intermagnet.org</a></li> </ol>
Dataset for the article entitled: "Revealing the strengthening contribution of stacking faults, dislocations and grain boundaries in severely deformed LPBF AlSi10Mg alloy"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>AlSi10Mg_HT320.ang - Heat treated sample</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>TKD data:</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>XRD data:</p> <p>AlSi10Mg_HT320.ASC - Heat treated sample</p> <p>AlSi10Mg_HT320_ECAP100.ASC - Heat treated ECAP processed sample</p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
Open the record for dataset details and reuse information.
Dataset of Evaluating the effects of biomass burning on severe haze formation in a megacity of Sichuan Basin, Southwestern China
<p><span>This dataset contain the concentrations of different chemical compositions, including organic aerosol, nitrate, sulfate, ammonium, chloride, BC, etc. The time series of different OA sources and meteorological parameters are also contained in the dataset.</span></p>
Effectiveness of Mannitol Use on Clinical Outcomes of Severe Traumatic Brain Injury Patients
<p>Data set for article entitled: </p> <p><span>Effectiveness of Mannitol Use on Clinical Outcomes of Severe Traumatic Brain Injury Patients</span></p>
Voltage-current measurements for several Heavy Metal concentrations, and their combinations
<p>These files contain the voltage-current measurements that have been used to train a neural network model to infer heavy metal concentrations within the Seawizard project. <br><br>IMPORTANT NOTICE: these files contain an artificial offset. Please, contact the owners before using these datasets. </p> <p>martina.moreno@eurecat.org </p> <p>ana.moya@eurecat.org</p> <p>gonzalo.vilella@eurecat.org</p>
Code and data for: Ladder fuels, not canopy volumes, consistently associated with forest wildfire severity even in extreme topographic-weather conditions
<p>Code and associated metrics for Hakkenberg et.al. 2024. Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions</p> <p>Contact chrishakkenberg@gmail.com for all questions and requests</p>
Temperature and chemical composition of combustion gases over reaction time in several combustion tests conducted at a lab pilot plant to characterise a SRF prepared for an aluminium scrap pre-heating system (REVaMP project)
<p>Open access to experimental data generated by the REVaMP project (GA 869882, Horizon 2020, European Union) along the research of the combustion of a SRF, prepared from ASR, to be used as alternative fuel in a scrap pre-heater at an aluminium refinery plant.Research pertaining to Task 1.1 (WP1), Deliverable D1. <br> Underlying data for the publication Acha, E.; Lopez-Urionabarrenechea, A.;Delgado, C.; et al. Combustion of a Solid Recovered Fuel (SRF) Produced from the Polymeric Fraction of Automotive Shredder Residue (ASR). Polymers 2021, 13, 3807. https://doi.org/10.3390/polym13213807. Data related to Figures 3, 4, 5 in the article and Figures S3 and S5 of Supplementary materials of the manuscript.</p> <p>Subject: Study of the combustion of a SRF prepared from ASR in a lab-scale pilot plant consisting of a tank reactor and a packed-bed tubular reactor arranged in series, to evaluate the effects on kinetics and thermodynamics of the temperature and the type and flow rate of the oxidiser. Real-time measurements of the temperature of the tank, temperature of the gases and of the concentrations of O<sub>2</sub>, CO<sub>2</sub>, CO, N<sub>2</sub>O, NO<sub>2</sub>, NO, NH<sub>3</sub>, SO<sub>2</sub>, CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, C<sub>2</sub>H<sub>4</sub>, C<sub>3</sub>H<sub>8</sub>, C<sub>6</sub>H<sub>14</sub>, CH<sub>2</sub>O, HCl and HF in the combustion gases produced in 5 combustion runs performed at different reaction conditions. Useful information for designing the operation conditions of the SRF combustion chamber of the scrap pre-heater and for defining the flue gas cleaning requirements.</p>
Subspecies and Distribution. V. t. tangalunga Gray, 1832 — Peninsular Malaysia, Sumatra, Borneo, several Indonesian islands (Amboina I, Banggi I, Langkawi I, Rhio-Lingga Archipelago, Bangka I, Karimata I & Sulawesi), and the Philippines; also two records from Java, but no evidence of native population. V. t. lankavensis Robinson & Kloss, 1920 — Malaysia (Langkawi I). in Viverridae
Subspecies and Distribution. V. t. tangalunga Gray, 1832 — Peninsular Malaysia, Sumatra, Borneo, several Indonesian islands (Amboina I, Banggi I, Langkawi I, Rhio-Lingga Archipelago, Bangka I, Karimata I & Sulawesi), and the Philippines; also two records from Java, but no evidence of native population. V. t. lankavensis Robinson & Kloss, 1920 — Malaysia (Langkawi I).
Subspecies and Distribution. A. t. trivirgata Gray, 1832 — Peninsular Thailand and Malaysia, Sumatra, and Borneo; also found on several small Indonesian Is. A. t. leucotis Horsfield, 1851 — NE India (Assam), Bangladesh, China (Yunnan), and Mainland SE Asia to the Isthmus of Kra. A. t. tnilineata Wagner, 1841 — Java. in Viverridae
Subspecies and Distribution. A. t. trivirgata Gray, 1832 — Peninsular Thailand and Malaysia, Sumatra, and Borneo; also found on several small Indonesian Is. A. t. leucotis Horsfield, 1851 — NE India (Assam), Bangladesh, China (Yunnan), and Mainland SE Asia to the Isthmus of Kra. A. t. tnilineata Wagner, 1841 — Java.
Distribution. Bhutan, China, India, Japan (Hokkaido), North and South Korea, Nepal, Laos, Mongolia, N Myanmar, Russia, Taiwan, and N Thailand. Introduced to several Japanese islands (Honshu, Kyushu & Shikoku Is). in Mustelidae
Distribution. Bhutan, China, India, Japan (Hokkaido), North and South Korea, Nepal, Laos, Mongolia, N Myanmar, Russia, Taiwan, and N Thailand. Introduced to several Japanese islands (Honshu, Kyushu & Shikoku Is).
Distribution. Throughout mainland SE Asia, from C & S Myanmar to W & SE Thailand, C & S Laos, Cambodia, C & S Vietnam, and Peninsular Malaysia; also present in Borneo, Sumatra, Java, Bali, Lombok, and several adjacent small islands. Most likely extinct from E Bangladesh, where it has not been reported for more than 40 years. Northern and western limits of its range are poorly known. in Manidae
Distribution. Throughout mainland SE Asia, from C & S Myanmar to W & SE Thailand, C & S Laos, Cambodia, C & S Vietnam, and Peninsular Malaysia; also present in Borneo, Sumatra, Java, Bali, Lombok, and several adjacent small islands. Most likely extinct from E Bangladesh, where it has not been reported for more than 40 years. Northern and western limits of its range are poorly known.
Distribution. Historically thought to occur in Cambodia, S Laos, SE Thailand, and W Vietnam, but perhaps wider. If still extant in the wild, probably restricted to several protected areas in E Cambodia. in Bovidae
Distribution. Historically thought to occur in Cambodia, S Laos, SE Thailand, and W Vietnam, but perhaps wider. If still extant in the wild, probably restricted to several protected areas in E Cambodia.
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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.