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562 results for “Mini”
Fabrication of Hydrogel Mini-Capsules as Carrier Systems - Extended data
<p>This folder contains the Extended Data to the paper "Fabrication of Hydrogel Mini-Capsules as Carrier Systems".</p> <ul> <li>ChargedCapsuleCaCO3.gif (time-lapse of an hydrogel capsule filled with CaCO3 to show the nature of the core through the release of the particles)</li> <li>ChargedCapsuleSiO2.jpg (image of an hydrogel capsule loaded with SiO2 microparticles)</li> <li>ChargedCapsuleSiO2_core.wmv (video of the particle in the picture "ChargedCapsuleSiO2.jpg" after shell dissolution)</li> <li>TutorialBeadsFabrication.mov (video tutorial showing how the fabrication of the core-shell hydrogel capsules is realized)</li> <li>TimelapseCapsuleDyeReleased.png (r<span>elease of a green dye from one capsule monitored over 30 minutes)</span></li> <li><span><span>CapsuleInsideSiliconeTube.mov (video of the capsule passing through the silicone tube)</span></span></li> <li><span>LowMeltAgaroseCapsule.png (images of a core-shell capsule fabricated using low-melting agarose)</span></li> </ul>
Polifonia Mini Textual Corpus
<p>The Polifonia Mini Textual Corpus is made of a balanced selection of sentences representative of the different periods and styles of the corpus. It was created to perform the transformation of sentences into AMR graphs. The reason why the Polifonia Mini Textual Corpus is made of sentences is twofold: (i) the Polifonia Textual Corpus' documents' length prevents them from fitting into GPU memory; (ii) AMR parsers' models are trained to transform no more than a few sentences into AMR graphs; therefore, their performance on large documents parsing is unreliable.</p> <p>Creating a sub-sample of sentences representative of all the modules of the Polifonia Textual Corpus allows for performing iterative validations of the text2AMR pipeline output. For example, it facilitates testing the algorithms developed to minimise the loss of information that segregating texts into sentences inherently brings (such as the loss of co-reference information). It also favours a more manageable Quality Assurance of the AMR graphs produced by the text2AMR models.</p> <p>When the results obtained on processing the Polifonia Mini Textual Corpus are considered satisfactory, we will apply the text2AMR pipeline to the Polifonia Textual Corpus in its entirety.</p> <p>Full description at: <a href="https://github.com/polifonia-project/Polifonia-Knowledge-Extractor">https://github.com/polifonia-project/Polifonia-Knowledge-Extractor</a></p>
MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"
<p>This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>.</p> <p>Hot jupiters (<em>P</em> < 10 d, <em>M</em> > 60 M<sub>⊕</sub>) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R<sub>⊕</sub>, 11.0 ± 2.4 M<sub>⊕</sub>) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (<span class="math-tex">\(8.14^{+0.31}_{-0.30}\)</span> R<sub>⊕</sub>, <span class="math-tex">\(81.7^{+4.7}_{-4.6}\)</span> M<sub>⊕</sub>) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, <em>V</em> = 10.98, <em>TESS</em> magnitude = 10.36), a metal-rich ([Fe/H] = <span class="math-tex">\(0.439^{+0.041}_{-0.043}\)</span>) G dwarf 174 pc away. <em>TESS</em> observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the <em>V</em> band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.</p>
Underwater temperature, light, and dissolved oxygen data from 3 mini-buoys in Lake Sunapee, NH, USA from June to October 2018
Three mini-buoys were deployed during a portion of the ice-off period of 2018 in Lake Sunapee, NH, USA with HOBO temperature sensors at various depths below the water’s surface. Temperature data were collected using HOBO pendant temperature and HOBO pendant temperature/light sensors at descending depths between 0.1m and the nearest whole and/or half meter increments below the water surface and above the sediment/water interface at 10 minute intervals. Two buoys (Georges Mills and Herrick Cove) also had miniDOT (PME) dissolved oxygen and temperature sensors placed 1.75 meters below the surface. The buoys were located in cove areas of the lake in the north east arm of the lake (Herrick Cove, 0.1m – 6.5m), the west side of the southern area of the lake near Lake Sunapee State Beach (State Beach, 0.1m – 2.5m) and the northwest arm of the lake (George’s Mills, 0.1m -7m). This data has been QAQC’d to remove obviously errant data and artifacts of buoy maintenance visits.
mini CURE-OR
<p><strong>File descriptions</strong></p> <ul> <li><strong>train.zip </strong>- the training set</li> <li><strong>test.zip </strong>- the test set</li> <li><strong>train.csv</strong> - the ground truth for the training images with the following information: <em>imageID, class, background, perspective, challengeType, challengeLevel</em></li> <li><strong>test.csv</strong> - the ground truth for the training images with the following information: imageID, class, background, perspective, challengeType, challengeLevel</li> </ul> <p><strong>Data fields</strong></p> <ul> <li><strong>imageID </strong>- an anonymous id unique to a given image</li> <li><strong>class </strong>- the class of the object in the given image: 1-10 <ul> <li>1: Canon camera</li> <li>2: Training marker cone</li> <li>3: Baseball</li> <li>4: Pan</li> <li>5: Toy</li> <li>6: LG Cell phone</li> <li>7: Hair brush</li> <li>8: DYMO Label maker</li> <li>9: Calcium bottle</li> <li>10: Shoes</li> </ul> </li> <li><strong>background </strong>- the background of the object <ul> <li>1: 2D white</li> <li>2: 2D living room</li> <li>3: 2D kitchen</li> </ul> </li> <li><strong>perspective </strong>- the perspective/orientation of the object <ul> <li>1: Front</li> <li>2: Left side - 90 degrees</li> <li>3: Back - 180 degrees</li> <li>4: Right side - 270 degrees</li> <li>5: Top</li> </ul> </li> <li><strong>challengeType </strong>- the type of generated challenging conditions <ul> <li>01: No challenge</li> <li>02: Resize</li> <li>03: Underexposure</li> <li>04: Overexposure</li> <li>05: Gaussian blur</li> <li>06: Contrast</li> <li>07: Dirty lens 1</li> <li>08: Dirty lens 2</li> <li>09: Salt & pepper noise</li> <li>10: Grayscale</li> <li>11: Grayscale resize</li> <li>12: Grayscale underexposure</li> <li>13: Grayscale overexposure</li> <li>14: Grayscale gaussian blur</li> <li>15: Grayscale contrast</li> <li>16: Grayscale dirty lens 1</li> <li>17: Grayscale dirty lens 2</li> <li>18: Grayscale salt & pepper noise</li> </ul> </li> <li><strong>challengeLevel </strong>- the level of generated challenging conditions <ul> <li>0: No challenge (01) and Grayscale (10) only - no challenge level</li> <li>1 - 4: the degree of a challenge from least to most</li> </ul> </li> </ul> <p>For more information about CURE-OR dataset, please refer to the <a href="https://ghassanalregib.info/software-and-datasets">webpage</a>.</p>
Fabrication of Hydrogel Mini-Capsules as Carrier Systems - Underlying data
<p>This dataset contains the Underlying Data to the paper "Fabrication of Hydrogel Mini-Capsules as Carrier Systems".</p> <ul> <li><span lang="EN-GB">images (folder containing the set of images used for the data analysis)</span></li> <li><span lang="EN-GB">microscopy_measurments.csv (measurements acquired with the microscope software)</span></li> <li><span lang="EN-GB">Software_ThicknessAnalysis.jl (</span>main script for reproducing the data analysis<span lang="EN-GB">)</span></li> <li><span lang="EN-GB">Functions_ThicknessAnalysis.jl (</span>collection of functions for the data analysis<span lang="EN-GB">)</span></li> <li><span lang="EN-GB">Manifest.toml and Project.toml (computational environment files)</span></li> <li><span lang="EN-GB">_init_.jl (utility script for reproducing the computational environment)</span></li> <li><span lang="EN-GB">README.md</span></li> </ul>
Mini-flyer: Arctic PASSION
<p>Arctic PASSION is contributing to improving the network of observing systems that monitor the environmental changes in the Arctic.</p> <p>We work closely with Indigenous and local communities, as well as scientific networks and decision-makers to co-create essential environmental information accessible to support sustainable development and climate change adaptation.</p>
ASTRI Mini-Array Instrument Response Functions (Prod2, v1.0)
<p><strong>Aim:</strong></p> <p>This data repository provides access to a set of Instrument Response Functions (IRFs) of the ASTRI Mini-Array, saved in a FITS data file. The IRFs can be used as input to science analysis tools for high-level scientific analysis purposes.</p> <p><strong>Citations:</strong></p> <p>In the case the present ASTRI Mini-Array Instrument Response Functions (IRFs) are used in a research project, we kindly ask to add the following acknowledgement in any resulting publication:</p> <p>"This research has made use of the ASTRI Mini-Array Instrument Response Functions (IRFs) provided by the ASTRI Project [citation]."</p> <p>Please use the following BibTex Entry for [citation] in the reference section of your publication: <a href="https://zenodo.org/record/6827882/export/hx">https://zenodo.org/record/6827882/export/hx</a></p> <p><strong>Instrument:</strong></p> <p>The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the <em>Observatorio del Teide</em> (Tenerife, Spain) [1]. The telescopes are an evolution of the dual-mirror ASTRI-Horn telescope, successfully installed and tested since 2014 at the INAF “M.C. Fracastoro” observing station in Serra La Nave (Mt. Etna, Italy) [2][3].</p> <p>The ASTRI Mini-Array is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with a differential sensitivity that surpass the one of current Cherenkov telescope facilities above a few TeV, extending the energy band well above hundreds of TeV [4].</p> <p>The main science goals of the ASTRI Mini-Array in the very high-energy (VHE) gamma-ray band encompass both galactic and extragalactic science [5][6][7]. Important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes are foreseen.</p> <p><strong>Monte Carlo Simulations:</strong></p> <p>The IRFs of the ASTRI Mini-Array were obtained from a dedicated Monte Carlo (MC) production (dubbed ASTRI Mini-Array Prod2, version 1.0). Air showers initiated by gamma rays, protons and electrons were simulated using the CORSIKA package [8] (version 6.99), while the response of the array telescopes was simulated using the sim_telarray package [9] (version 2018-11-07).</p> <p>The layout of the ASTRI Mini-Array telescopes considered in the MC simulations is based on the actual telescope positions at the Teide Observatory site (28.30°N, 16.51°W, 2390 m a.s.l.). The nominal telescope pointing configuration, in which all telescopes point to the same sky position, was assumed in all MC simulations. Air showers produced by the primaries were simulated as coming from a zenith angle of 20° and an azimuth angle of 0° and 180° (corresponding to telescope pointing directions toward the geomagnetic North and South, respectively). Although not-negligible differences in performance (on the order of ≤15% at a zenith angle of 20°) are found between the two azimuthal pointing directions, the final IRFs were obtained by averaging between the two directions. Finally, all MC simulations were generated with a night sky background (NSB) level corresponding to dark sky conditions at the Teide Observatory site.</p> <p><strong>Monte Carlo data reduction and analysis:</strong></p> <p>The MC simulations were reduced and analysed with A-SciSoft [10][11] (version 0.3.1), the scientific software package of the ASTRI Project. The calibration and reconstruction of the MC events were achieved with the standard methods implemented in the data reduction pipeline (see [10][11] for more details). In particular, the background rejection and energy reconstruction were achieved with a procedure based on the Random Forest method [12], while the arrival direction of each shower was estimated from a weighted intersection of the major axes of the images from different telescopes. After the full reconstruction of the MC events, the background (proton and electron) events were re-weighted according to recent experimental measurements of their spectra, while gamma-ray events with a power-law gamma-ray spectrum with a photon index of 2.62. This approach follows a similar procedure adopted in [13].</p> <p>The final analysis cuts were based on the background rejection, shower arrival direction, and event multiplicity parameters. They were defined, in each considered energy bin and off-axis bin, by optimising the flux sensitivity for 50 hr exposure time. Then, five standard deviations (5σ, with σ defined as in Eq. 17 of [14]) were required for a detection in each energy bin and off-axis bin, considering the same exposure time (as in the cut optimization procedure) and a ratio of the off-source to on-source exposure equal to 5. In addition, the signal excess was required to be larger than 10 and at least 5 times the expected systematic uncertainty in the background estimation (assumed to be ∼1%). It should be noted that these analysis cuts, based on the best flux sensitivity, do not provide the best angular and energy resolution achievable by the system. Other analysis cuts, which take into account both differential flux sensitivity and angular/energy resolution in the cut optimization process, may actually provide better performance [4].</p> <p><strong>Instrument Response Functions (IRFs):</strong></p> <p>The IRFs are saved in a FITS data file [15] which contains the following quantities (FITS tables): effective collection area ("EFFECTIVE AREA" table), angular resolution ("POINT SPREAD FUNCTION" table), energy resolution ("ENERGY DISPERSION" table), and residual background rate ("BACKGROUND" table). These quantities are provided as a function of the energy and the off-axis. The energy bins are logarithmic and range between 10<sup>-0.7 </sup>~ 0.2 TeV and 10<sup>2.5 </sup>~ 316 TeV. Five energy bins per decade are used for the angular resolution and residual background rate, while ten energy bins per decade for the effective collection area. In the case of energy resolution, the energy migration matrix is provided with a much finer energy binning. The off-axis bins are linearly spaced between 0° and 6°, with a bin width equal to 1°. In the case of the residual background rate, a 2-dimensional squared spatial binning is used, which ranges between 0° and 6° with a bin width equal to 0.2° in each direction.</p> <p>The IRFs can be used as input to science analysis tools and, in particular, are compliant with the input/output (I/O) data format requested by the science analysis tools Gammapy [16] and ctools [17].</p> <p><strong>Dataset:</strong></p> <p>The dataset consists of one file: "astri_100_43_008_0502_C0_20_AVERAGE_50h_SC_v1.0.lv3.fits".</p> <p>The naming convention is: astri_[ARRAY_ID]_[ORIG_ID]_[REL_ID]_[PACKET_TYPE]_[CLASS_CUT]_[ZENITH]_[AZIMUTH]_[ EXPOSURE_TIME]_[AIM]_[VERSION].lv3.fits</p> <p>where:</p> <ul> <li>[ARRAY_ID] = 100 (100 = ASTRI Mini-Array with 9 telescopes)</li> <li>[ORIG_ID] = 43 (4 = INAF-OAR; 3 = AIV/AIT MC simulations)</li> <li>[REL_ID] = 008 (008 = MC prod2, v1.0)</li> <li>[PACKET_TYPE] = 0502 (0502 = IRF3)</li> <li>[CLASS_CUT] = C0 (C0 = cuts based on sensitivity maximisation)</li> <li>[ZENITH] = 20 [deg]</li> <li>[AZIMUTH] = AVERAGE [deg]</li> <li>[EXPOSURE_TIME] = 50h</li> <li>[AIM] = SC (SC = SCience)</li> <li>[VERSION]= v1.0</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>This work was conducted in the context of the ASTRI Project thanks to the support of the Italian Ministry of University and Research (MUR) as well as the Ministry for Economic Development (MISE) with funds specifically assigned to the Italian National Institute of Astrophysics (INAF). We acknowledge support from the Brazilian Funding Agency FAPESP (Grant 2013/10559-5) and from the South African Department of Science and Technology through Funding Agreement 0227/2014 for the South African Gamma-Ray Astronomy Programme. The Instituto de Astrofisica de Canarias (IAC) is supported by the Spanish Ministry of Science and Innovation (MICIU). This work has also been partially supported by H2020-ASTERICS, a project funded by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n. 653477. This work has gone through the internal ASTRI review process.</p> <p>We would also like to thank the computing centres that provided resources for the generation of the Monte Carlo (MC) simulations used to produce the ASTRI Mini-Array Instrument Response Functions (IRFs) released in this work:</p> <ul> <li>CAMK, Nicolaus Copernicus Astronomical Center, Warsaw, Poland</li> <li>CIEMAT-LCG2, CIEMAT, Madrid, Spain</li> <li>CYFRONET-LCG2, ACC CYFRONET AGH, Cracow, Poland</li> <li>DESY-ZN, Deutsches Elektronen-Synchrotron, Standort Zeuthen, Germany</li> <li>GRIF, Grille de Recherche d’Ile de France, Paris, France</li> <li>IN2P3-CC, Centre de Calcul de l’IN2P3, Villeurbanne, France</li> <li>IN2P3-CPPM, Centre de Physique des Particules de Marseille, Marseille, France</li> <li>IN2P3-LAPP, Laboratoire d'Annecy de Physique des Particules, Annecy, France</li> <li>INFN-FRASCATI, INFN Frascati, Frascati, Italy</li> <li>INFN-T1, CNAF INFN, Bologna, Italy</li> <li>INFN-TORINO, INFN Torino, Torino, Italy</li> <li>MPIK, Heidelberg, Germany</li> <li>OBSPM, Observatoire de Paris Meudon, Paris, France</li> <li>PIC, port d’informacio cientifica, Bellaterra, Spain</li> <li>prague_cesnet_lcg2, CESNET, Prague, Czech Republic</li> <li>praguelcg2, FZU Prague, Prague, Czech Republic</li> <li>UKI-NORTHGRID-LANCS-HEP, Lancaster University, United Kingdom</li> </ul> <p><strong>References:</strong></p> <ol> <li>Scuderi, S. et al., "The ASTRI Mini-Array of Cherenkov telescopes at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 52–68 (2022).</li> <li>Giro, E. et al., "First optical validation of a Schwarzschild Couder telescope: the ASTRI SST-2M Cherenkov telescope", A&A 608, A86 (Sept. 2017).</li> <li>Lombardi, S. et al., "First detection of the Crab Nebula at TeV energies with a Cherenkov telescope in a dual-mirror Schwarzschild-Couder configuration: the ASTRI-Horn telescope", A&A 634, A22 (Feb. 2020).</li> <li>Lombardi, S. et al., "Performance of the ASTRI Mini-Array at the Observatorio del Teide", in [37th International Cosmic Ray Conference. 12-23 July 2021. Berlin], 884 (Mar. 2022).</li> <li>Vercellone, S. et al., "ASTRI Mini-Array core science at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 1–42 (2022).</li> <li>D’Aì, A. et al., "Galactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 139–175 (2022).</li> <li>Saturni, F. et al., "Extragalactic Observatory Science with the ASTRI Mini-Array at the Observatorio del Teide", Journal of High Energy Astrophysics 35, 91–111 (2022).</li> <li>Heck, D. et al., [CORSIKA: a Monte Carlo code to simulate extensive air showers.], Report FZKA 6019 (1998).</li> <li>Bernlöhr, K., "Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray", Astropart. Phys. 30, 149–158 (Oct. 2008).</li> <li>Lombardi, S. et al., "ASTRI SST-2M prototype and mini-array data reconstruction and scientific analysis software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy IV], Chiozzi, G. and Guzman, J. C., eds., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 9913, 991315 (July 2016).</li> <li>Lombardi, S. et al., "ASTRI data reduction software in the framework of the Cherenkov Telescope Array", in [Software and Cyberinfrastructure for Astronomy V], Guzman, J. C. and Ibsen, J., eds., Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series 10707, 107070R (July 2018).</li> <li>Breiman, L., "Random Forests", Machine Learning 45, 5–32 (Jan. 2001).</li> <li>Cherenkov Telescope Array Observatory, & Cherenkov Telescope Array Consortium. (2021). CTAO Instrument Response Functions - prod5 version v0.1 (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5499840</li> <li>Li, T.-P. and Ma, Y.-Q., "Analysis methods for results in gamma-ray astronomy", ApJ, 272, 317 (1983)</li> <li>Pence, W. D. et al., "Definition of the Flexible Image Transport System (FITS), version 3.0", A&A 524, A42 (Dec. 2010).</li> <li>Deil, C. et al., "Gammapy - A prototype for the CTA science tools", in [35th International Cosmic Ray Conference (ICRC2017)], International Cosmic Ray Conference 301, 766 (Jan. 2017).</li> <li>Knödlseder, J. et al., "GammaLib and ctools. A software framework for the analysis of astronomical gamma- ray data", A&A 593, A1 (Aug. 2016).</li> </ol>
Comprehensive Mini-Database of the Northern Hemisphere's Winter Sky: 100 Raw Images from Ensenada, Mexico
<p>We carried out several test sessions for data collection to adjust the settings of our optical system. From October 2022 to June 2023, we executed numerous sessions to assemble our primary catalog, capturing an extensive array of sky views. A total of 100 sky observations were recorded from various directions without restrictions. These sessions were held at the peak of a hill where CICESE, our research institute, is situated at coordinates 31°52′21.5′′ N 116°40′11.8′′ W in Ensenada, Baja California, Mexico. This location was chosen because it is relatively free from urban light pollution and noise, despite its proximity to the city outskirts. This position minimizes city light interference on one side, slightly reducing light pollution, although image quality was occasionally compromised by the light pollution and facility lighting.</p> <p>Using the ASI Studio software, we captured high-resolution images of 5496 × 3672 pixels without employing pixel binning to achieve the highest possible resolution. The camera's settings were adjusted to an exposure time of 0.5 seconds and standard gain, with the lens focused at infinity and an aperture set at f/4. This setup enabled us to detect significant background noise and numerous areas that could potentially contain stars.</p> <p>More information about the article is in the:</p> <p><a href="https://doi.org/10.3390/aerospace10090748">https://doi.org/10.3390/aerospace10090748</a></p>
ROADMAP - Mini Webinar Series
<p>The ROADMAP project worked on rethinking the use of antimicrobials in livestock production systems. In a mini webinar series, we have asked task leaders to summarise their key learnings from the project. This has resulted in the following six episodes:</p> <ol> <li>Massimo Canali (UNIBO): "Stakeholders' behaviour and strategies towards AMU"</li> <li>Lee-Ann Sutherland (HUT): "Identifying actors' motivations in the use and reduction of antimicrobials"</li> <li>Sophie Molia (CIRAD): "Creating impact from the assessed strategies"</li> <li>Mette Vaarst (AU): "Co-building levers and incentives"</li> <li>João Sucena Afonso (ULIV): "Key learnings on the impact of alternatives in livestock and aquaculture production"</li> <li>Bernadette Oehen (FiBL): "Implementing innovative strategies"</li> </ol> <p>All mini webinars can be watched here: https://www.youtube.com/playlist?list=PLW5PYxzlSCfzzdqn2n6-eCxfkGhRAT6Qy</p>
SSH CENTRE - Mini-reports : Focus groups on "Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030"
<p>SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality. </p><p>SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.</p><p>This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.</p><p>The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe. </p><p>Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes. </p>
Segmentation masks mini-MIAS
<p>This dataset provides manually created segmentation masks of the images in the mini-MIAS dataset by J Suckling et al[1] (available at http://peipa.essex.ac.uk/info/mias.html). The masks are saved as nrrd files with pixel-wise ground truth for background (0), breast (1), and pectoral muscle (2) (when present). This dataset is created for the development of a mammogram segmentation model[2].</p> <p>Segmentation masks were created in three steps, first initialization of the breast boundary by Otsu thresholding[3], second a pectoral muscle initialization with Otsu thresholding, and lastly a manual adjustment of the mask. The pectoral muscle initialization was done by re-applying the Otsu thresholding method after excluding the background. Finally, each segmentation mask was checked visually and adjusted manually using ITK-SNAP 3.6.013[4] by one of four medical imaging scientists with experience in mammography. </p> <p>[1] J Suckling et al<em>,</em> "The Mammographic Image Analysis Society Digital Mammogram Database" Exerpta Medica. International Congress Series 1069, 375-378 (1994)<br>[2] S.D. Verboom et al., "Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors", Journal of Medical Imaging, <strong>11</strong>(1), 014001 (2023)<br>[2] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Trans. Syst. Man. Cybern. <strong>9</strong>(1), 62–66 (1979)<br>[3] P. A. Yushkevich et al., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability," Neuroimage <strong>31</strong>(3), 1116–1128 (2006)</p>
Figs 5–7 in A mini-review of advances in the study of the evolution of Perdix species
Figs 5–7. Morphological comparison on three species of Perdix (Pictures from http://image.baidu.com/).
Figs 1–4 in A mini-review of advances in the study of the evolution of Perdix species
Figs 1–4. Phylogenies trees of Perdix. 1. Monophyletic group consisting of Perdix, Meleagrididae, and Tetraoninae in the supertree (Eo et al., 2009). 2. Based on morphological and zoogeographical data (Zheng, 1978). 3. Based on C-mos + Cyt b + ND2 (Bao et al., 2010). 4. Based on Cyt b + ND2 (Bao et al., 2010).
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Dataset: WillScot Mobile Mini Holdings Corp. (WSC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
JASMINE mini-Mock survey dataset
<p>Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a satellite mission that measures the precise positions and motions of stars in the Galactic nucleus region and inspects a periodic subtle dimming caused by exoplanets orbiting around M-type dwarf stars.</p> <p>We have generated small-scale JASMINE Galactic Centre survey data, JASMINE mini-Mock Survey (JmMS) data, for testing and validating JASMINE astrometric data analysis, especially for correction of optical distortion. For preliminary software validation, we design a simplified small-scale mock survey for “plate analysis”, where stellar motions and the variation in image distortion are treated as negligible within a dataset.</p> <p>To this end, we select a 1′×1′-target region around the Galactic center. We assume that JASMINE observes 4 fields per one orbit. Two of these fields overlap half of their field-of-view in the Galactic longitude direction, and another two fields overlap by half of their field-of-view in the Galactic latitude direction. The fields of these observations are randomly selected, but at least one of these 4 fields in one orbit covers the selected target region. In this mini-mock survey data, we consider 100 orbits. These data would be valuable for us to validate whether our astrometric analysis code can achieve the expected astrometric accuracy by correcting the image distortion using the assumption that the stellar position in the sky and the higher-order image distortion do not change during the observational time, ∼ 50 min, of one orbit. We summarise how to generate the mock data from the mini JASMINE Galactic Centre survey.</p> <p>The JASMINE mini-survey dataset contains the following files:</p> <ol> <li><code>readme.pdf</code>: A document explaining how the dataset is generated.</li> <li><code>jasmine_validation_augumented_source_catalog.fits.gz</code>: A FITS binary table of the source catalog that contains the ground truth astrometric parameters.</li> <li><code>jasmine_validation_reference_catalog.fits.gz</code>: A FITS binary table of the reference catalog that contains the astrometric parameters resampled from the source catalog.</li> <li><code>jasmine_validation_survey_strategy.fits.gz</code>: A FITS binary table of the survey strategy that describes the observation schedules with the telescope pointings and position angles.</li> <li><code>jasmine_validation_observation-vanilla_e4.0_20240530.tar.gz</code>: An archive of the JmMS dataset in case the telescope has no image distortion and the focal length does not change throughout the mission. The measurement errors are set to ~4.0 mas for all the sources.</li> <li><code>jasmine_validation_observation-distortion_e4.0_20240602.tar.gz</code>: An archive of the JmMS dataset in case the telescope suffers from image distortion and the focal length changes with every plate. The measurement errors are set to ~4.0 mas for all the sources.</li> </ol>
FIGURE 1 in Mini DNA barcodes reveal the details of the foraging ecology of the largehead hairtail, Trichiurus lepturus (Scombriformes: Trichiuridae), from São Paulo, Brazil
FIGURE 1 | Diagram showing the taxonomic composition of the prey items identified in the stomach of the largehead hairtail, Trichiurus lepturus, collected off the coast of São Paulo state in southeastern Brazil.
Linked collectors and determiners for: Molecular phylogeny reveals strong biogeographic signal and two new species in a Cape Biodiversity Hotspot endemic mini-radiation, the pygmy geckos (Gekkonidae: Goggia).
Natural history specimen data linked to collectors and determiners held within, "Molecular phylogeny reveals strong biogeographic signal and two new species in a Cape Biodiversity Hotspot endemic mini-radiation, the pygmy geckos (Gekkonidae: Goggia)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c958120b-cf83-4dd6-9560-1f783c6c9680">https://bionomia.net/dataset/c958120b-cf83-4dd6-9560-1f783c6c9680</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c958120b-cf83-4dd6-9560-1f783c6c9680">https://gbif.org/dataset/c958120b-cf83-4dd6-9560-1f783c6c9680</a>. Formatted as a Frictionless Data package.
Mini Talks/Videos about Open Science from OLS-2 Cohort
<p><strong>We aim to present important topics in Open Science (e.g. open Licence, open review, open access, open sources, agile, open protocols, ...) within 10-15 minutes videos with subtitles and translations. </strong></p> <p>All resources are cited from the <strong>Open Life Science (OLS-2) </strong>and available on <a href="https://www.youtube.com/channel/UCs12-ZgnDJOWIWN3Vo1XHXA">Open LifeSci YouTube channel</a>. This project was initiated by <a href="https://twitter.com/talarify?lang=en">Talarify</a> and <a href="https://twitter.com/OpenSciSaudi">Open Science Community in Saudi Arabia</a> to facilitate learning of Open science practices to <strong>novice learners</strong> and to make OLS videos and captions accessible, re-useable, and encourage further translation in other languages beyond English and Arabic.</p> <p><strong>Individual videos can be downloaded from the GitHub repository:</strong></p> <ul> <li><a href="https://www.youtube.com/watch?v=Zj8EWGq5Wkk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=1">Introduction to Open Life Sciences</a></li> <li><a href="https://www.youtube.com/watch?v=gQx-au72h04&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=2">Open Canvas for Project Strategy</a></li> <li><a href="https://www.youtube.com/watch?v=YKCKDAJ1RDU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=3">Roadmapping for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=2xGFF6qHOb8&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=4">A Primer on Open Licenses</a></li> <li><a href="https://www.youtube.com/watch?v=kodsPukJcE0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=5">README for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=XXuy9suO4Kw&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=6">Contributing Guidelines and Codes of Conduct for Open Projects</a></li> <li><a href="https://www.youtube.com/watch?v=VLpJTBhuotM&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=7">Agile and Iterative Project Management Methods</a></li> <li><a href="https://www.youtube.com/watch?v=RMeGH8AnEIU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=8">Open Scientific Code in Research</a></li> <li><a href="https://www.youtube.com/watch?v=xDDdJsHa078&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=9">Open Data</a></li> <li><a href="https://www.youtube.com/watch?v=3er0NjjHGHE&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=10">Open Education and Training</a></li> <li><a href="https://www.youtube.com/watch?v=Fw6B3kdy5Ow&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=11">Preprint in the Context of Open Science</a></li> <li><a href="https://www.youtube.com/watch?v=hva-oTapSWU&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=12">Open Protocols</a></li> <li><a href="https://www.youtube.com/watch?v=llp1KD7T93s&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=13">Making training materials FAIR in 10 steps</a></li> <li><a href="https://www.youtube.com/watch?v=nqvDU-skyoQ&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=14">FAIR research software</a></li> <li><a href="https://www.youtube.com/watch?v=9Z4CGxpN4pY&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=15">Universal theme</a></li> <li><a href="https://www.youtube.com/watch?v=4H9YDPwaHls&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=16">Career Guidance in Academia</a></li> <li><a href="https://www.youtube.com/watch?v=EOAJeq-q6W0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=17">Career Path and Guidance</a></li> <li><a href="https://www.youtube.com/watch?v=gY64DenZBM0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=18">Open Leadership</a></li> <li><a href="https://www.youtube.com/watch?v=gY64DenZBM0&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=18">Persona and Pathways</a></li> <li><a href="https://www.youtube.com/watch?v=Mh3r7wZiDyI&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=20">Mountain of Engagements</a></li> <li><a href="https://www.youtube.com/watch?v=_mLXXyx6nyk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=21">Inclusion can't be an afterthought</a></li> </ul> <p>They are also available on a <a href="https://www.youtube.com/watch?v=Zj8EWGq5Wkk&list=PL1CvC6Ez54KCcs99wV3eex1v5GUry6Yb7&index=1">playlist on YouTube</a>.</p> <p><strong>Contributing</strong> 💝</p> <p>We welcome all contributions to improve this project especially first-timers to expand the translation!</p> <p><strong>You don't need to know git to start contributing, we use <a href="https://crowdin.com/project/ols2">Crowdin localisation</a>, which enables you to translate strings of SRT files while watching the video and adding content to your translation. More details are added to <a href="https://github.com/open-life-science/ols2-cohort-talks-and-transcripts/blob/main/CONTRIBUTING%E2%80%8B.md">our Contribution Guide</a>.</strong></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.