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2,025 results for “R&D”
Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education
<p>Supplementary materials of the paper "Agile Minds, Innovative Solutions, and Industry–Academia Collaboration: Lean R&D Meets Problem-based Learning in Software Engineering Education"</p>
Fig. 2 in Naturalization Of Melanoides Tuberculata And Tarebia Granifera (Thiaridae, Gastropoda) M O L L U S K S U N D E R T H E H Y D R O E C O L O G I C A L Conditions Of Zaporizhzhya Npp Cooling Pond
Fig. 2. Mollusks in the hydrobiological frame 25x25 cm.
Fig. 1 in M Or P Ho L O Gi Ca L Va R Iati On An D P Op U Lat Io N Structure Of The Natterjack Toad, Epidalea Calamita, In Northern Part Of The Range In Belarus
Fig. 1. Natterjack toad Epidalea calamita in Belarus.
Fig. 7 in P H E N O T Y P I C P L A S T I C I T Y A N D N U C L E A R D N A Polymorphism Of Two Differing Pinus Sylvestris L. Open-Pollinated Families Originating From The Same Population
Fig. 7. Heterozygosity at 5 loci among the investigated Scots pine families.
Fig. 2 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 2. Sampling localities of C. odoratus.
Fig. 9 in Altitudinal Variation In Population Density, Body Size And Morphometric Structure In C A R A B U S O D O R At U S S H I L, 1996 (C O L E O P T E R A: Carabidae)
Fig. 9. Results of each trait variation in C. odoratus at different altitudes.
Fig. 4 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig. 4. Effect of grain size (2.2-2.0 mm) on oat grain yield.
Fig.3 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig.3. Effect of plant height on oat grain yield.
Fig. 2 in G R A I N Y I E L D A N D I T S F O R M I N G Pa R A M E T E R S Variations Of Oat Cultivars
Fig. 2. Grain yield of tested cultivars (n=19) from 2012 to 2014, t ha-1.
Fig. 1 in A N E W G E N U S A N D S P E C I E S O F L O N G - H O R N E D Beetles Of The Tribe Apomecyni Lacordaire, 1872 (Coleoptera: Cerambycidae: Lamiinae) From The Philippines
Fig. 1. The distribution map of L. anichtchenkoi sp. n. in the Philippines archipelago.
Fig. 3. Genus Lamprobityle Heller, 1923 in A N E W G E N U S A N D S P E C I E S O F L O N G - H O R N E D Beetles Of The Tribe Apomecyni Lacordaire, 1872 (Coleoptera: Cerambycidae: Lamiinae) From The Philippines
Fig. 3. Genus Lamprobityle Heller, 1923: L. rugulata (Vives, 2009).
Fig. 2 in Potential herbicidal effect of synthetic chalcones on the initial growth of sesame, Sesamum indicum L., and brachiaria, Urochloa decumbens (Stapf) R. D. Webster
Fig. 2. Chemical structures of synthesized chalcones and their respective yields.
Bridge Inspecting with Unmanned Aerial Vehicles R&D
<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>
Semi-leptonic ttbar full-event unfolding R&D dataset
<p>This dataset was generated for the purpose of developing unfolding methods that leverage generative machine learning models. It consists of two pieces: one piece contains events with the Standard Model (SM) production of a top-quark pair in the semi-leptonic decay mode, and the other contains events with top-quark pair production modified by a non-zero EFT operator. The SM dataset contains 15,015,000 events, and the EFT dataset contains 30,000,000. Both datasets store the following event configurations:</p> <ul> <li>Parton level: configurations of all partons that result from the matrix element calculation done using MadGraph</li> <li>Particle level: configurations of all “truth” jets and leptons that result from the parton shower and hadronization modelled using Pythia</li> <li>Detector level: configurations of all reconstruction level jets and leptons, measured by a detector simulated with Delphes and the default CMS detector card.</li> </ul> <p>Each of these configurations is stored in a dedicated group as described below. Throughout, the units of energy and transverse momentum are GeV. For more details on the generation of this dataset, see Ref. [1].</p> <p><strong>Parton level data:</strong></p> <ul> <li>No phase space requirements are placed on the events at parton level. </li> <li>The kinematics of the top, anti-top, W+, W-, and all decay products are contained in groups entitled top, antitop, Wp, and Wm respectively. Each of these contains the kinematics of the parton itself in a group called “particle”, as well as the kinematics of two daughter particles, in groups called “d1” and “d2”. In the case of the tops, these daughters are the W’s and b quarks. In the case of the W’s, these are two light quarks, or a lepton and a neutrino. The “pid” vector contains the PDGID for a given particle, used to identify its type. </li> <li>One detail is that the W’s “particle” description is not always the same as the description of the same W stored as the daughter of the tops. This results from when the W radiates some parton before decaying. </li> </ul> <p><strong>Particle level data:</strong></p> <ul> <li>At particle level all leptons and jets are required to have $p_T > 25$ GeV and absolute pseudo rapidity $|\eta| < 2.5$. </li> <li>Events at particle level are required to have at least one electron or muon and at least 4 jets, of which at least two are b-tagged. Event which pass or fail this criteria are marked by the vector contained in the group “mask”.</li> <li>Electrons and muons are stored in separate groups. Each group contains a vector “mask” which is true only if there is a true particle-level electron or muon in the event, and false if this entry is zero padding. </li> <li>Jets are clustered from stable particle level objects using the anti-kt algorithm with a radius parameter of 0.5. Jet information is stored in the group “jets”, and true jets in the event are again denoted by a true value in the vector “mask”, and zero-padding is marked by a false value. Jets additionally contain a vector “btag” which is 1 if the jet is b-tagged with the default Delphes prescription, and 0 if not.</li> <li>Information on the missing transverse momentum (MET) is contained in the group “met”. The “met” vector gives the magnitude, and the “phi” vector gives the direction in phi of the missing transverse momentum.</li> <li>In addition to the information on the jets, leptons, and MET, the particle level data also contain the configurations for the hadronic top, leptonic top, and ttbar system. These configurations are determined assuming the pseudo-top jet parton assignment algorithm, which is a common method used by LHC experiments when analyzing semileptonic ttbar events.</li> </ul> <p><strong>Detector level data:</strong></p> <ul> <li>Requirements for leptons and jets are the same as for the particle level data.</li> <li>The event selection is the same as the particle level data. Events which pass the selection are again denoted by a true value in the vector “mask”.</li> <li>The data for the leptons, jets, and MET are stored analogously to particle level</li> <li>The configurations of the top quarks and ttbar system are not pre-computed at detector level, since ideally a generative unfolding method would not assume a given jet-carton assignment algorithm when it is being trained. However if the user wishes to pursue such an application, the relevant configurations can be obtained by running the pseudo-top algorithm [2].</li> </ul> <p><strong>Citations:</strong></p> <p>[1] - <a href="https://arxiv.org/abs/2404.14332">https://arxiv.org/abs/2404.14332</a></p> <p>[2] - <a href="https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions">https://twiki.cern.ch/twiki/bin/view/LHCPhysics/ParticleLevelTopDefinitions</a></p>
CMV Antiviral Prevention Strategies in D+R-Liver Transplants ("CAPSIL")
ClinicalTrials.gov study NCT01552369. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Supplementary material 1 from: Bragança PHN, van Zeeventer RM, Bills R, Tweddle D, Chakona A (2020) Diversity of the southern Africa Lacustricola Myers, 1924 and redescription of Lacustricola johnstoni (Günther, 1894) and Lacustricola myaposae (Boulenger, 1908) (Cyprinodontiformes, Procatopodidae). ZooKeys 923: 91-113. https://doi.org/10.3897/zookeys.923.48420
Species localities and Genbank Acession numbers: acession numbers in bold refers to sequences developed in the present study
Subspecies and Distribution. . c. canuti Thomas & Wroughton, 1909 — Java (three localities), and Nusa Barong ail'd Bali Is. . c. timorensis R. E. Goodwin, 1979 — Timor I. in Rhinolophidae
Subspecies and Distribution. . c. canuti Thomas & Wroughton, 1909 — Java (three localities), and Nusa Barong ail'd Bali Is. . c. timorensis R. E. Goodwin, 1979 — Timor I.
Supplementary material 1 from: Short G, Claassens L, Smith R, De Brauwer M, Hamilton H, Stat M, Harasti D (2020) Hippocampus nalu, a new species of pygmy seahorse from South Africa, and the first record of a pygmy seahorse from the Indian Ocean (Teleostei, Syngnathidae). ZooKeys 934: 141-156. https://doi.org/10.3897/zookeys.934.50924
Genetic distance analysis (uncorrected p distances) of COI sequence data from H. nalu, H. bargibanti, H. denise, H. japapigu, and H. pontohi
Supplementary material 11 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893
Identifying priority knowledge needs for implementing nature-based solutions-results from Malta and other Mediterranean islands
Supplementary material 9 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893
Management and restoration of Mediterranean wetlands to provide ecosystem services and other benefits
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.