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FIGURE 7. Living animals. A in Filling gaps in the knowledge of Goniodorididae taxa (Mollusca, Gastropoda, Nudibranchia) with description of seven new species
FIGURE 7. Living animals. A. Naisdoris aurornata sp. nov. (CASIZ 186113) B. Naisdoris aurornata sp. nov. (CASIZ 186128). C. Naisdoris labalsaensis sp. nov. (WAM S72659). D. Naisdoris labalsaensis sp. nov. (WAM S72657). E. Naisdoris vitiligata sp. nov. (CASIZ 186126). F. Naisdoris vitiligata sp. nov. (ZCR.MOL.15410). (Photos A, B, E by T. Gosliner, C, D by G. Cobb, F by T. C. Hoon)
FIGURE 6. Buccal bulb and reproductive system. A–B in Filling gaps in the knowledge of Goniodorididae taxa (Mollusca, Gastropoda, Nudibranchia) with description of seven new species
FIGURE 6. Buccal bulb and reproductive system. A–B. Murphydoris polkadotsa sp. nov. (QM MO 86038). A. Buccal bulb. B. Reproductive system. C–D. Naisdoris aurornata sp. nov. (CASIZ 186113). C. Buccal bulb. D. Reproductive system. E–F. Naisdoris labalsaensis sp. nov. (WAM S72700). E. Buccal bulb. F. Reproductive system. G–H. Naisdoris vitiligata sp. nov. (ZCR.MOL.15410). G. Buccal bulb. H. Reproductive system. Abbreviations: am, ampulla; bc, bursa copulatrix; bp, buccal pump; fgm, female gland mass; hd, hermaphroditic duct; oe, esophagus; op, esophageal pump; p, penis; pr, prostate; ra, radular sac; rs, receptaculum seminis; sgl, salivary gland; ud, uterine duct; va, vagina; vd, vas deferens. Scale bars: 1 mm.
Data set for the study "Perception Disparity between Women and Men on the Gender Gap in STEM at a Spanish University"
<p>Database for the study "Perception" carried out in the project "Engineering with a gender perspective" financed by the Women's Institute of the Ministry of Equality of the Spanish Government, call for proposals PAC 2021 INMUJERES (reference number 37/7ACT/21).<br>An adaptation of the GENCE 2.0 questionnaire (DOI: 10.5281/zenodo.2550690) was used to collect the data. </p>
Figure 7 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 7: Research output for the Southeast Asian region by thematic area presented as decadal totals.
Figure 4 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 4: Halophila sp. 2 collected in 1997 at a mangrove area of Teluk Sepinong, Sandakan, Sabah, Malaysia. Photo credit: © Japar Sidik.
Figure 2 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 2: Marine provinces and ecoregions of Southeast Asia, based on Spalding et al. (2007). Provinces are made out of ecoregions with the following codes: 20108 Northern Bay of Bengal; 20109 Andaman and Nicobar Islands; 20110 Andaman Sea Coral Coast; 20111 Western Sumatra; 20112 Gulf of Tonkin; 20114 South China Sea Oceanic Islands; 20115 Gulf of Thailand; 20116 Southern Viet Nam; 20117 Sunda Shelf/Java Sea; 20118 Malacca Strait; 20119 Southern Java; 20120 Cocos-Keeling/Christmas Island; 20126 Palawan/North Borneo; 20128 Sulawesi Sea/Makassar Strait; 20129 Halmahera; 20130 Papua; 20131 Banda Sea; 20132 Lesser Sunda; 20133 Northeast Sulawesi; 20139 Arafura Sea.
Figure 3 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 3: Halophila major from Tanjung Adang Laut, Sungai Pulai estuary, Johor (refer also to Nguyen et al. 2014 for morphological and genetic identification of the species). Photo credit: © Muta Harah.
Figure 8 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 8: Roadmap for addressing conservation challenges facing seagrass in the Southeast Asian region. Challenges can have multiple solutions and these solutions can contribute to a final aim. The challenges are listed at the bottom of the figure in red ovals and the final aims are at the top of the figure in blue boxes. Intermediate solutions are presented in the middle in green boxes. Dotted lines represent linkages betweeen the challenges and the intermediate solutions, with each dotted line colour coded specific to a challenge. Intermediate solutions that contribute to a final aim are joined by dashed and solid lines and colour coded.
Figure 1 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 1: Total population by country in Southeast Asia. Population figures derived from www.worldatlas.com.
Figure 6 in Seagrass in Southeast Asia: a review of status and knowledge gaps, and a road map for conservation
Figure 6: Research output per decade by country/territory. Numbers based on searches on Web of Science and updated from Ooi et al. (2011a).
GAP interatomic potential for PtAu:H nanoparticle simulation
<p><strong>Gaussian approximation potential</strong> (GAP) [1] for the <strong>platinum-gold-hydrogen </strong>system. It can be safely used to generate PtAu nanoparticles throughout the whole composition range and to hydrogenate them (add adsorbed H on the surface). For any other system, the accuracy and stability of the potential should be specifically tested (e.g., against reference DFT calculations). It has been fitted with <strong>gap_fit </strong>[2] by generating a new database of atomic structures containing:</p> <ol> <li>bulk PtAu structures, including 3x3x3 supercells with substitutional alloying as determined by cluster-expansion configurations generated by the CASM code [10];</li> <li>PtAu surface slabs, with random alloying and H adsorption at different coverages;</li> <li>iteratively generated PtAu clusters (nanoparticles), following a similar approach as in Ref. [3], and adding H coverage to those.</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [4] using the VASP code [5,6]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [7,8]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and <strong>TurboGAP</strong> [9]. The reference publication for this potential will be added in time (e.g., when a preprint is a valable, and then updated when a journal publication is available).</p> <p>Financial support from the Research Council of Finland and computational resources from CSC (the Finnish IT Center for Science) and Aalto University's Science-IT project are gratefully acknowledged.</p> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>S. Klawohn, J.P. Darby, J.R. Kermode, G. Csányi, M.A. Caro, and A.P. Bartók. J. Chem. Phys. 159, 174108 (2023).</li> <li> <div>J. Kloppenburg, L.B. Pártay, H. Jónsson, and M.A. Caro. J. Chem. Phys. 158, 134704 (2023).</div> </li> <li>J.P. Perdew, K. Burke and M. Ernzerhof. Phys. Rev. Lett. 77, 3865 (1996).</li> <li>VASP: <a href="http://vasp.at/">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi/">http://turbogap.fi</a></li> <li>https://prisms-center.github.io/CASMcode_docs</li> </ol>
What is the skill gap
Open the record for dataset details and reuse information.
A high-quality gap-filled daily ETo dataset for China during 1951-2021 from synoptic stations
<p><span>The reference evapotranspiration (ETo) is essential for water-consuming in agriculture and land-water cycle research. The synoptic data from meteorological stations can provide reliable ground data for ETo estimation with the FAO-56 Penman-Monteith equation. However, the five primary variables this equation needs, including maximum temperature (Tmax), minimum temperature (Tmin), sunshine duration (SSD), wind speed (Wind), and relative humidity (RH), often experience severe data loss due to force majeure events in synoptic data. The data loss would directly introduce severe data gaps to the complex records for ETo. Machine learning algorithms can fill various data gaps with low error rates, however, to achieve high data quality, the algorithms must be selected properly to deal with the distinct types of data loss and train independently. Here, based on the data characters, we investigated and classified data gaps from the synoptic dataset into 2 major types: the common, minor data loss gaps including Tmax loss/Tmin loss/SSD loss/Wind loss/RH loss/Wind and SSD loss/Wind and RH loss, and the other 19 types of data loss which is more severe in information loss but barely occurred. Our results show that the XGBoost model achieved the best accuracy in all 3 machine learning models with high statistic levels. For the other 19 types of data gaps, the LSTM models were trained separately for each site and achieved average R², RMSE, and nRMSE at 0.9, 0.5 mm d-1, and 38% for the total 2419 stations. Thus, we propose a high-quality, gap-filled daily ETo dataset during 1951-2021 for China with the proportion of large errors (the data with daily ETo errors more than 1.5 mm d-1) below 0.2%. Our results also reveal that the entanglement degree between synoptic variables varies a lot from region to region in China.</span></p>
Data from: The spatiotemporal evolution and formation mechanism of the digital economic gap: Based on the case of China
<p>We analyzed the formation mechanism of digital economic gap (DEG), measured the DEGs at four levels (the gaps in information and communication technology accessibility, application skill, digital economic outcome, and efficiency), and explored its spatiotemporal evolution in China by using DEA–Malmquist index method, Gini Coefficent method, Kernel density, and Geodetector. Data from 263 cities in China between 2011 and 2019 were collected. The results demonstrated that (1) The four levels of DEGs showed different trends. The first-, second- and third- level DEGs showed ceiling effects, and the fourth-level DEG oscillated upward. (2) The distribution location of the four levels of DEGs varied. The first- and second-level DEGs shifted at a stable low degree. The third-level DEG increased steadily and polarized. The fourth-level DEG increased steadily and formed a multi-polarization trend, with one strong polar. (3) The long-term transfer trend of the DEGs at four levels changed little, and showed a phenomenon of "club convergence". (4) As for the formation of DEGs, the first-level DEG was influenced by most factors and was education- and policy-driven; the second-level DEG was profit-driven; the third-level DEG was profit- and education-driven; the fourth-level DEG was human resource-driven.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2023)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 24-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2023. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems
<p>Replication package for the paper "Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries in Software Package Ecosystems "</p>
Replication package for: "Trading stocks builds financial confidence and compresses the gender gap"
<p>This package contains replication files for results using experimental data from "Trading stocks builds financial confidence and compresses the gender gap" by Saumitra Jha and Moses Shayo, to be published in <em>The Economic Journal</em>.</p>
Band gaps and a few properties more of metals, metal oxides and carbon materials
<p>We achieved to create a database of electronic structure properties of bulk materials (metals, metal oxides, carbon) with a good relative accuracy compared to experimental values and minimal computational cost using density functional theory and semi-empirical quantum mechanical methods.</p>
FIGURE 6 in Filling the gap of whip spider distribution in Asia: Phrynichus persicus sp.n. (Arachnida, Amblypygi), a new Phrynichidae from Iran
FIGURE 6. DistributiOnAl mAp Of All Phrynichus KArsch, 1879 species. LOcAlities And cOOrdinAtes Were gAthered frOm BAstAWAde et al. (2005); MAquArt et al. (2016); WeygOldt (1998, 2003, 2009); WeygOldt et al. (2002) And Huber (pers. cOm.). The recOrd Of Phrynichus exophthalmus Whittick, 1940 in SOuth AfricA is dOubtful (WeygOldt 1998).
FIGURE 5 in Filling the gap of whip spider distribution in Asia: Phrynichus persicus sp.n. (Arachnida, Amblypygi), a new Phrynichidae from Iran
FIGURE 5. BAsi- And distitibiA Of leg IV shOWing number And pOsitiOn Of trichObOthriA. Right rectAngle shOWs frOntAl series And left rectAngle shOWs cAudAl series Of trichObOthriA. ScAle bArs: 1mm.
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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.