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Figure 9 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 9. Comparative reconstructions of the cervical complex in several cetaceans with neck muscle origins discussed in this paper. The reconstructions concerned Messapicetus gregarius (MUSM 2548) (a), Ninoziphius platyrostris (MNHN SAS 941) (b), Hyperoodon ampullatus (SNM CN1x) (c), Mesoplodon bidens (MNHN A14519) (d), Inia geoffrensis (SNM CN1x) (e), and Xiphiacetus cristatus (IRSNB 3240-M.361) (f). Dotted lines correspond to broken parts.
Figure 1 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 1. Set of linear measurements taken for the study exemplified in Hyperoodon ampullatus. (a) Skull in ventral view; (b) skull in lateral view; (c) scapula in lateral view; (d) humerus in lateral view; (e) radius in lateral view.
Figure 2 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 2. Cervical vertebrae of the specimen MUSM 2548, Messapicetus gregarius. Axis in anterior (a), posterior (b), and ventral view (c); C5–C6 in anterior (d), posterior (e), and dorsal view (f); C7 in anterior (g), posterior (h), and dorsal view (i).
GBM Targeted Search Data Release for GRB 241112B
<div> <p>The following Files include:</p> <ul> <li>The summed GBM NaI detector lightcurve,</li> <li>The individual GBM NaI detector lightcurves,</li> <li>The BGO detector lightcurves, </li> <li>The GBM NaI detector lightcurves sliced by energy.</li> <li>The waterfall plot (all energy) showing the most significant timescale of the event,</li> <li>The waterfall plot with the three different spectral models (soft, normal and hard, see below).</li> <li>The localization of GRB 241112B, showing the 3 and 1-sigma contours.</li> <li>The healpix map for "Event 1", which is the event found by the Targeted Search and shown in these plots. </li> </ul> <p><br>The definitions for soft, normal and hard spectra are:<br><br>"soft" spectrum (Band function with Epeak = 70 keV, alpha = -1.9, beta = -3.7) for a GRB.<br>"normal" spectrum (Band function with Epeak = 230 keV, alpha = -1.0, beta = -2.3) for a GRB.<br>"hard" spectrum (Comptonized function with Epeak = 1500 keV, alpha = -0.5) for a GRB. </p> <p> </p> </div>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Searching for HI around MHONGOOSE Galaxies via Spectral Stacking
<p>Stacked spectra obtained from spectrally stack around 14 nearby galaxies observed at 1.4 GHz as part of the MHONGOOSE survey (<a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...688A.109D/abstract" target="_blank" rel="noopener">de Blok et al., 2024</a>). The reference paper is available at <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241111584V/abstract" target="_blank" rel="noopener">NASA-ADS</a>.</p> <p><em>First page: </em>detections in the stacked spectra of the shallow cubes. Each panel shows a single-track stacked spectrum (in blue) and its 9-channel boxcar smoothed version (in red). The detected source is highlighted. The horizontal grey dashed lines are the ±σ level for the unsmoothed spectrum, while the black dashed line is the 0-flux level. The vertical black dashed-dotted line is, instead, the 0-km/s reference velocity. The galaxy name and the cell size are provided in the bottom-left corner, while at the bottom-right is reported if the detection was also visually identified in its corresponding cube.<br><em>Second page</em>: detections in the stacked spectra of the full-depth cubes. Each panel shows a single-track stacked spectrum (in blue) and its 9-channel boxcar smoothed version (in red). The detected source is highlighted. The horizontal grey dashed lines are the ±σ level for the unsmoothed spectrum, while the black dashed line is the 0-flux level. The vertical black dashed-dotted line is, instead, the 0-km/s reference velocity. The galaxy name and the cell size are provided in the bottom-left corner, while at the bottom-right is reported if the detection was also visually identified in its corresponding cube.</p>
Data release: Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes
<p>The data required to reproduce the analyses of "Searching for binary black hole sub-populations in gravitational wave data using binned Gaussian processes" (<a href="https://arxiv.org/abs/2404.03166" target="_blank" rel="noopener">arxiv:2404.03166</a>). The main inference code can be found at <a href="https://github.com/AnaryaRay1/gppop/tree/spin-dev" target="_blank" rel="noopener">https://github.com/AnaryaRay1/gppop/tree/spin-dev </a> (commit: <a href="https://github.com/AnaryaRay1/gppop/commit/ee5ffc421e2c96eeed15a0e0d3839da42b982842">ee5ffc</a>). To reproduce the analyses, follow the instructions at <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts">https://github.com/AnaryaRay1/bbh-subpopulations-scripts</a> (commit <a href="https://github.com/AnaryaRay1/bbh-subpopulations-scripts/commit/de88f931d8c1a2cb31ad2fa9d6fdf9a5a00a3c3b">de88f93</a>). Frozen versions of these repositories that were used to generate all the results are available as part of this data release, in the files "gppop_spin_dev_ee5ffc421.tar.gz" and "bbh-subpopulations-scripts_de88f931.tar.gz" respectively.</p>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Reproduction package for "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data"
<p>This is a basic reproduction package for the paper "Searching for low radio-frequency gravitational wave counterparts in wide-field LOFAR data" by Gourdji et al. (2021) published in MNRAS. It describes the software and settings used to obtain the final data products of the analysis. It also includes a Jupyter notebook and required data to reproduce the tables and figures of this paper.</p>
Reproducibility Package: Automata-Driven Partial Order Reduction and Guided Search for LTL
<p>Reproducibility package for the paper <strong>Automata-Driven Partial Order Reduction and Guided Search for LTL Model Checking</strong> accepted for VMCAI'22.</p> <p>Contains scripts, 64-bit Linux binaries, and the dataset used to run the experiments detailed in the paper, a source code snapshot used to compile those binaries, as well as scripts for processing the results into the tables seen in the paper. The file README.md contains details on running the various parts of the artifacts, assuming a Linux environment.</p>
Reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif"
<p>This is a basic reproduction package for the paper "A search for radio emission from double-neutron star merger GW190425 using Apertif".</p>
Diffuse Supernova Neutrino Background search at Super-Kamiokande (arXiv:2109.11174, PRD 104, 122002)
<p>This is the data release for the PRD 104, 122002 article about the Diffuse Supernova Background search at Super-Kamiokande. The figures which can be reproduced with this release are listed in the README of the data_release folder.</p> <p>The spectral_analysis_npy.SKMC.tar.gz folder contains the files needed to reproduce the spectral analysis in section VII of the paper. To incorporate it to the spectral analysis code please follow the instructions given in the README of <a href="https://github.com/soso128/spectral_analysis">the spectral analysis Github folder</a>.</p>
Data for: Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics
<p>Data for publication: A. Scheidegger, J. Vanicek, and N. Golubev, Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics, https://doi.org/10.1063/5.0076609</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Search for merger ejecta emission in Short Gamma Ray Bursts from very late time radio observations
<p>Coalescence of inspiral binary neutron stars (BNS) system, giving rise to short Gamma Ray Bursts (GRBs), are one of the most probable candidates for Gravitational Waves (GWs). If the resultant product of the merger is a millisecond magnetar, a significant proportion of the rotational energy deposited to emerging ejecta that produce late time radio brightening from the interaction with the surrounding ambient medium. Detection of this late-time radio emission from short GRBs can have profound implications for understanding the physics of the progenitor. This study presents the deepest and an extensive search for radio emission at late times following a short GRB to date incorporating proper frequency regime, wider observation span and relativistic correction. Five short GRBs were observed with the Giant Meter Wave Radio Telescope (GMRT) at 1250, 610, and 325 MHz band $\sim$ 2 - 11 years since the burst to search for radio emission from the merger ejecta. The estimated upper limits at the burst location are used to constrain the parameters of the burst and its surrounding environment. The magnetar model, with appropriate modifications, constrains the number density of the ambient medium for these bursts to be between $10^{-4}$ - $10^{-2}$ $cm^{-3}$. Our analysis rules out a stable magnetar with an energy of $10^{53}$ erg for four out of the five GRBs in our sample.</p>
Dataset for "Searching for a source without gradients: how good is infotaxis and how to beat it"
<p>Dataset containing the results presented in "Searching for a source without gradients: how good is infotaxis and how to beat it".</p>
F4TCNQ and TTF on approximate supported graphene, 3D surface adsorbate search with BOSS
<p>These datasets were employed in the manuscript:</p> <p>[1] J. Järvi, M. Todorović, and P. Rinke, Efficient modeling of organic adsorbates on oxygen-intercalated graphene on Ir(111), <em>Phys. Rev. B</em> 105, 195304 (2022). DOI: 10.1103/PhysRevB.105.195304</p> <p>The two datasets feature DFT sampling of adsorption configurations for F<sub>4</sub>TCNQ and TTF on approximate supported graphene (Gr) using the Bayesian Optimization Structure Search (BOSS) method. The 3D configurational searches were conducted for each molecule over two molecular translations (<em>x, y</em>) and an in-plane rotation (<em>gamma</em>). The search range of on-surface translation (<em>x, y</em>) is [0, 1] in fractional coordinates of the Gr unit cell. The in-plane rotation (<em>gamma</em>) is done in the range [0, 180] deg. Energy (<em>E</em><sub>ads</sub>) is the adsorption energy of F<sub>4</sub>TCNQ or TTF on the approximate supported Gr, computed with DFT. For additional details on search coordinates and DFT calculations please refer to [1].<br> <br> The 3D models of the adsorption energy surfaces were constructed with 500 energy points for each molecule, the full lists are provided in the datasets. Each calculation is denoted with an ID number (001-500). The raw data for each calculation can be found in the NOMAD repository with the corresponding ID, DOI: 10.17172/NOMAD/2022.04.07-1</p>
APPENDIX A - SEARCH PROTOCOLE AND PAPERS LIST - Cognitive Neuroscience's Transposition in STEAM Classroom: A Systematic Literature Review
<p>An integrative literature review was performed corresponding to PRISMA’ protocol in order to integrate issues over an innovative curriculum through cognitive sciences didactic transposition in STEAM classrooms. The search action was handled using various international databases. Each paper was selected alongside the PICO strategy and a defined selection criterion. Analysis of the literature between 2000 and 2021 is presented.</p>
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:50:45 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Google Scholar search record: ?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
File generated: Search date, time, timezone: 2022-04-15 17:34:33 (Europe/London) Search parameters: All these words: crayfish None of these words: This exact word or phrase: "water chemistry" Any these words: Language: en Between these years: and Number of pages exported: 1 Starting from page: 1 Citations included: TRUE Citations included: TRUE Search only in the title: FALSE Authors: Source: GS links generated: https://scholar.google.co.uk/scholar?start=0&q=crayfish+%22water+chemistry%22&hl=en&as_vis=0,5&as_sdt=0,5
Search results for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)
<p>This datasets is the result of the search for scientific production about CRIS (Current Research Information System) in 3 databases: WoS, Scopus and Dimensions (2000-2020)</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.