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FIGURE 6 in Hidden in plain sight: two new species of decapod crustaceans (Palaemonidae and Porcellanidae) discovered in the Red Sea on a university campus in Saudi Arabia
FIGURE 6. Enosteoides habibi sp. nov., paratype, male (cl 5.5 mm, cw 5.2 mm) from Thuwal, Saudi Arabia (specimen lost, see text); A, carapace, dorsal (showing main tubercles and granular ridges, some omitted); B, same, right lateral; C, frontal area, ventral; D, same, anterior; E, branchiostegium and pterygostomial flap, ventrolateral; F, sternum, ventral; G, telson and sixth pleonite, dorsal (ventral view as tucked under cephalothorax); H, left antennule, basal article, ventral; I, right third maxilliped, lateral (ventral); J, right second pereiopod, lateral; K, same, distal portion of propodus and dactylus, lateral; L, right third pereiopod, lateral; M, right fourth pereiopod, lateral; N, right fifth pereiopod, lateral; O, right second pleopod, posterior (ventral).
FIGURE 2 in Hidden in plain sight: two new species of decapod crustaceans (Palaemonidae and Porcellanidae) discovered in the Red Sea on a university campus in Saudi Arabia
FIGURE 2. Palaemonella jamila sp. nov., paratype, female (pocl 4.4 mm, cl 7.8 mm) from Thuwal, Saudi Arabia (specimen lost, see text); A, right first pereiopod, lateral; B, same, distal portion of carpus and chela, mesial; C, same, lateral, slightly enlarged; D, right second pereiopod, lateral; E, same, mesial (coxa and basis omitted); F, same, distal portion of carpus and chela, lateral; G, same, distal portion of chela palm and fingers slightly open, lateral; H, right third pereiopod, lateral; I, same, distal portion of propodus and dactylus, lateral; J, same, mesial; K, right fourth pereiopod, lateral; L, right fifth pereiopod, lateral; M, same, distal portion of propodus and dactylus, lateral; N, same, mesial.
FIGURE 1 in Hidden in plain sight: two new species of decapod crustaceans (Palaemonidae and Porcellanidae) discovered in the Red Sea on a university campus in Saudi Arabia
FIGURE 1. Palaemonella jamila sp. nov., paratype, female (pocl 4.4 mm, cl 7.8 mm) from Thuwal, Saudi Arabia (specimen lost, see text); A, anterior region of carapace and cephalic appendages, dorsal; B, same, right lateral; C, carapace, left lateral; D, rostrum, right lateral; E, pleon, posterior part of fifth and sixth pleonite, right lateral; F, telson, dorsal; G, right antennal scaphocerite, dorsal; H, right mandible, lateral; I, same, mesial; J, right maxillule, lateral; K, right maxilla, lateral; L, right first maxilliped, lateral; M, right second maxilliped, lateral; N, same, detail of epipod and podobranch, mesial; O, right third maxilliped, lateral; P, right uropod, dorsal. Setae omitted in C, D, F, G and P.
FIGURE 5. Palaemonella aliska Marin, 2008 in Hidden in plain sight: two new species of decapod crustaceans (Palaemonidae and Porcellanidae) discovered in the Red Sea on a university campus in Saudi Arabia
FIGURE 5. Palaemonella aliska Marin, 2008, female (pocl 3.4 mm, cl 5.5 mm) from Al Fahal reef off Thuwal, Saudi Arabia, FLMNH UF 36078; shrimp in life, dorsolateral (right second pereiopod regenerating). Photograph: A. Anker.
Universal Dynamic Fitting of Magnetic Resonance Spectroscopy - Data
<p>Original data for the in vivo experiments included in the article Universal Dynamic Fitting of Magnetic Resonance Spectroscopy by William T Clarke, Clémence Ligneul, Michiel Cottaar, Betina Ip and Saad Jbabdi (Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK).</p> <p>This data accompanies the online <a href="https://git.fmrib.ox.ac.uk/wclarke/fsl-mrs-dynamic-fitting">code repository</a>. Note that this repository contains preprocessed versions of this data already.</p> <p>Two files are included:</p> <ol> <li>fmrs_invivo_unprocessed_data.zip - NIfTI-MRS formatted data relating to the fMRS in vivo example in this work. Data is originally from <a href="https://doi.org/10.1016/j.neuroimage.2017.04.030">10.1016/j.neuroimage.2017.04.030</a>.</li> <li>dwmrs_invivo_original_data.zip = matlab .mat data relating tot he dwMRS in vivo example in this work. Data is originally from <a href="https://doi.org/10.1016/j.neuroimage.2019.02.046">10.1016/j.neuroimage.2019.02.046</a>.</li> </ol> <p>If you use this data in your work please cite this work (Universal Dynamic Fitting of Magnetic Resonance Spectroscopy) once available and the originating publications.</p>
Data of Semi-Arid Climate and Environment Observatory Station of Lanzhou University (SACOL) (2019.12.09-2021.12.31)
<p><strong>Data Description</strong></p> <p>The PM<sub>2.5</sub> mass concentrations were observed by using a tapered element oscillating microbalance machine (TEOM, Model RP-1400A, Thermo Scientific, USA) with a temporal resolution of 1 minute. An aethalometer (Model AE31, Magee Scientific, USA) with a PM<sub>2.5</sub> inlet was used to measure the aerosol light absorption coefficients at 370, 470, 520, 590, 660, 880, and 950 nm. Aerosol scattering coefficients of PM<sub>2.5</sub> at 450, 550, and 700 nm wavelengths were observed by using an integrating nephelometer (Model 3563, TSI, USA) with a time resolution of 10 seconds. The micro-pulse lidar was installed approximately 50 m north of the tethered balloon observation site and used to observe atmospheric echoes within 20 km vertically and then invert parameters such as the aerosol σ and aerosol optical depth. The detailed parameters of lidar are shown in Table 1.</p> <p>A tethered balloon (volume: 10 m<sup>3</sup>; payload: 8 kg) was used to carry the following equipment: (1) MA200 micro-aethalometer (Magee Scientific, USA), (2) TSI 9306 optical particle counter (OPC, TSI, USA), (3) KZXLT-II sounding system from the Institute of Atmospheric Physics, and (4) GPSMAP 639sc GPS (GARMIN, USA). Sounding observations were made at 02:00, 08:00, 11:00, 14:00, 17:00, and 20:00 each day from December 9 to 31, 2019. A total of 91 vertical profiles were obtained for each of the available absorbing aerosols, particle number concentrations, and meteorological parameters.</p> <p>The MA200 measures light absorption at 5 wavelengths (375, 470, 528, 625 and 880 nm), and a dual-spot® compensation for loading effect correction is available. The MA200 observations have been converted to 550 nm in this data based on the AAE during the observation period. OPC was used to measure the particle number concentrations and particle number size distribution in the 0–10 μm size range. The temporal resolutions of MA200 and OPC were ~ 10 s, and their vertical resolutions were ~ 10 m. KZXLT-II sounding system was used to measure the vertical distribution of meteorological elements. GPS was used to measure real-time altitude.</p> <p> </p> <p><strong>Table 1.</strong> Main parameters of micro-pulse lidar</p> <table> <tbody> <tr> <td> <p>Projects</p> </td> <td> <p>Parameters</p> </td> </tr> <tr> <td> <p>Detector</p> </td> <td> <p>Avalanche laser APD, photon counting model</p> </td> </tr> <tr> <td> <p>Observation band</p> </td> <td> <p>532 nm</p> </td> </tr> <tr> <td> <p>Pulse frequency</p> </td> <td> <p>2500 Hz</p> </td> </tr> <tr> <td> <p>Pulse energy</p> </td> <td> <p>3~4 µJ</p> </td> </tr> <tr> <td> <p>Maximum observation range</p> </td> <td> <p>15 km</p> </td> </tr> <tr> <td> <p>Time resolution</p> </td> <td> <p>1 min</p> </td> </tr> <tr> <td> <p>Vertical Resolution</p> </td> <td> <p>30 m</p> </td> </tr> </tbody> </table> <p> </p>
Lifestyle and sense of coherence: A comparative analysis among university students in different areas of knowledge
<p>Background: The concept of health has undergone profound changes. Lifestyle Medicine (LSM) consists of therapeutic approaches that focus on the prevention and treatment of diseases. It follows that the quality of life of university students directly affects their health and educational progress.</p> <p>Experimental Methodology: Socioeconomic, lifestyle (LS), and sense of coherence (SOC) questionnaires were administered to college students from three different areas. The results were analyzed for normality and homogeneity, followed by ANOVA variance analysis and Dunn and Tukey post hoc test for multiple comparisons. Spearman's correlation coefficient evaluated the correlation between lifestyle and sense of coherence; p values < 0.05 were considered statistically significant. </p> <p>Results: The correlation between LS and SOC was higher among males and higher among Medical and Human sciences students compared to Exact sciences. Medical students' scores were higher than Applied sciences and Human sciences students on the LS questionnaire. Exact science students' scores on the SOC questionnaire were higher than Human sciences students. In the LS areas related to alcohol intake, sleeping quality, and behavior, there were no differences between the areas. However, women scored better in the nutrition domain and alcohol intake. The SOC was also higher in men compared to women. </p> <p>Conclusion: The results obtained demonstrate in an unprecedented way in the literature that the correlation between the LS and SOC of college students varies according to gender and areas of knowledge, reflecting the importance of actions on improving students' quality of life and enabling better academic performance.</p>
Dataset for: Faculty Publication Trends in a Japanese National University: A Diachronic Document Analysis
<p>Dataset for paper titled: Faculty Publication Trends in a Japanese National University: A Diachronic Document Analysis</p>
Physics-driven universal twin-image removal network for digital in-line holographic microscopy - dataset
<p>Dataset (Matlab files) containing holograms and reference reconstructions employed in:<br> <br> M. Rogalski, P. Arcab, L. Stanaszek, V. Micó, C. Zuo and M. Trusiak, "Physics-driven universal twin-image removal network for digital in-line holographic microscopy", Submitted 2023<br> <br> This dataset should be used together with the codes present at:<br> https://github.com/MRogalski96/UTIRnet</p>
Continuous data of air temperature, relative humidity, and air pressure collected at Kyoto University in January 2022
<p>I present the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room (latitude: 35.02938 N, longitude: 135.78347 E, elevation: 60.82 m), Graduate School of Science, Kyoto University in January 2022. The values of air temperature, relative humidity, and air pressure were obtained every 1 second by a BME280 sensor (Bosch Sensortec GmbH) on a RT-USB-THP module (RT Corporation). The data were time-stamped and recorded using the Tera Term software on a Windows PC, which was synchronized in time with the NTP server of NICT (ntp.nict.jp).</p> <p>photo.zip contains the photographs of the Kyoto-A gravity measurement room taken in December 2022. The RT-USB-THP module and the Windows PC were located on the basement rock of the Kyoto-A absolute gravity point.</p> <p>data.zip contains the continuous data of air temperature, relative humidity, and air pressure collected at the Kyoto-A gravity measurement room. Each file of 2201??.txt stores the data obtained from 11:55 JST of the corresponding day to 11:54 JST of the next day, according to the operating time of the Tera Term software. The columns in 2201??.txt are as follows from left to right: year, month, day, hour, minute, second, air temperature [degC], air pressure [hPa], and relative humidity [%]. Note that 220101.txt was not recorded due to the failure of the Windows PC on January 1, 2022.</p> <p>figure.zip contains the graphs of air temperature, relative humidity, and air pressure drawn using the GMT4 software. Each graph of 2201??.png shows the time variation in air temperature, relative humidity, and air pressure from 0:00 JST to 24:00 JST of the corresponding day. tonga.png shows the air pressure variation from 12:00 JST on January 15 to 11:54 JST on January 16, and its original data is available as 220115.txt in data.zip. The pressure change of about 2 hPa was observed around 20:40 JST on January 15, associated with the propagation of the Lamb wave generated by the massive eruption of the Hunga Tonga-Hunga Ha’apai volcano.</p>
Universities, university residences and others
<p>Report results for Mathematic TFG</p>
Universities of Applied Sciences' EU research project participation through the lens of differentiation (dataset)
<p>Dataset of the publication "Universities of Applied Sciences’ EU research project participation through the lens of differentiation" (DOI: <a href="https://doi.org/10.1093/scipol/scad048">10.1093/scipol/scad048</a>)</p>
Ethical approval committee of College of Medicine/ University of Al-Qadisiyah
<p>The ethical approval committee of College of Medicine/ University of Al-Qadisiyah </p>
Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale
<p>Pythia computed all single mutations of <em>E.coli</em> proteome, high quality high quality of Swiss-Prot structures and thermophilic proteins used in analysis.</p>
Supplementary code and data for "Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks"
<p>Supplementary code and data for the conference article</p> <blockquote> <p>"Tackling universal properties of minimal trap spaces of boolean networks" Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, and Loïc Paulevé Proceedings of CMSB 2023, LCNS, Springer.<a href="https://doi.org/10.1007/978-3-031-42697-1_11"> https://doi.org/10.1007/978-3-031-42697-1_11</a></p> </blockquote> <p>Requirements</p> <ul> <li> <p>Python at least 3.9</p> </li> <li> <p><a href="https://github.com/bnediction/bonesis">bonesis</a> (v0.5.6) - <a href="https://doi.org/10.5281/zenodo.7984628">https://doi.org/10.5281/zenodo.7984628</a></p> <p>With pip:</p> <ul> <li><code>pip install bonesis==0.5.6</code></li> </ul> <p>With conda:</p> <ul> <li><code>conda install -c potassco -c colomoto bonesis=0.5.6</code></li> </ul> </li> <li> <p>for the synthesis: <a href="https://github.com/sybila/biodivine-aeon-py/">AEON.py</a></p> <p>With pip:</p> <ul> <li><a href="https://rustup.rs">https://rustup.rs</a></li> <li><code>pip install biodivine-aeon</code></li> </ul> <p>With conda</p> <ul> <li><code>conda install -c daemontus biodivine_aeon</code></li> </ul> </li> </ul> <p> </p> <p>Usage</p> <ul> <li> to execute the CEGAR approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_cegar.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>The first parameter is the .bnet file, the second one is the marker (in JSON), and the third is the maximum number k of components in a perturbation. To fix the marker, it is also possible to use a special node (ex: p) in the bnet file and require a marker '{"p":1}'. In the Moon22 dataset, information about uncontrollable components are available, one can use <code>--exclude '["Apoptosis","Growth_Arrest","Proliferation"]'</code>. If the marker is given as a component, it must be involed in the uncontrollable list.</p> <p>Moreover, the markers of the Moon22 dataset are contained in the bnet files in a special component <code>p</code>. Concerning the Trappist dataset, the possible markers are contained in files .markers, the user can choose between them.</p> <p>The dataset synthetic_bns contains random generated BNs. Also in this case, different possible markers are contained in files .markers.</p> <ul> <li>to execute the Complementary approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_complementary.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the Enumeration & Filtering approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_trapspaces_naive.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the CEGAR approach for a synthesis problem:</li> </ul> <pre><code>python synthesis.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.aeon '{"p":1}' </code></pre> <p>The first parameter is the prior knowledge graph in an .aeon file and the second one is the marker (in JSON). Another option is <code>--maxclause</code> which allow one to specify the maximum integer number of clauses authorised in local functions (by default is 128). The option <code>--no-canonic</code> leads to specify the criteria for the refinement of the under-approximation (3 for TS(y)|=M ; 1 for \exists y s.t. TS(y)!=TS(x) or 0 to obtain just a different candidate solution).</p>
Universal Qudit Gate Synthesis for Transmons. Open data set
<p>Data supporting the original figures 2, 5, 7, 8, 9, 10, 11 and 12 of the related publication.</p>
data used in study of "A universal programmable Gaussian Boson Sampler for drug discovery"
<p>1. Data used for analysis the average photon number distribution</p> <p>2. Data used for analysis the relation between squeezing level (r) and pump power (p)</p>
GloUTCI-M: A Global Monthly 1 km Universal Thermal Climate Index Dataset from 2000 to 2022
<p>The GloUTCI-M comprises global monthly UTCI data at a spatial resolution of 1km, spanning from March 2000 to October 2022. The dataset is expressed in degrees Celsius (°C) and is stored as an integer type (Int16). To utilize it appropriately, one must divide the values by 100.</p>
Data set for article: Intimate Partner Violence and Emotional Exhaustion: A Female Gender Perspective in the University Environment
<p>Data set for article: Intimate Partner Violence and Emotional Exhaustion: A Female Gender Perspective in the University Environment </p>
Code for "Experimental certification of contextuality, coherence and dimension in a programmable universal photonic processor"
<p>Code written, and data collected as part of the paper "Experimental certification of contextuality, coherence and dimension in a programmable universal photonic processor". The code corresponds to the data generated by a quadratic SDP solved using CVXPY.</p>
ScienceDex guides
Understand access before you commit
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.